text:
\section{Introduction}
\IEEEPARstart{P}OINT cloud has been widely adopted in practical applications such as virtual/augmented reality \cite{park2008multiple}, medical 3D reconstruction \cite{cheng2020morphing}, automatic driving \cite{cui2021deep}, and video post-production \cite{mekuria2016design} due to its ability of representing the 3D world. Consequently, plenty of works have been carried out to deal with point cloud classification \cite{grilli2017review,ku2018joint,wang2019frustum,vora2020pointpainting,xie2020pi,yoo20203d,chen2020object}, detection \cite{cui2021deep}, and segmentation \cite{cheng2021sspc,xu2020geometry}. However, point cloud quality assessment (PCQA) has gained less attention. PCQA aims to accurately predict the visual quality level of point clouds, which is vital for providing useful guidelines for simplification operations and compression algorithms in applications such as metaverse and virtual/augmented reality (VR/AR) \cite{dionisio20133d} to not negatively impact users' quality of experience (QoE). Moreover, the point clouds representing vivid objects/humans are usually more complex in geometric structure and contain large amounts of dense points along with color attributes, which makes the PCQA problem very challenging.

\begin{figure}[t]
    \centering
    \includegraphics[width=8.1cm]{figs/introduction.pdf}
    \caption{Examples of reflected distortions. The point clouds can explicitly reveal the geometry down-sampling distortion while failing to recognize texture noise unless the projections are involved, which raises the need for multi-modal perception. The projections are directly rendered from the distorted 3D point clouds via the Open3D \cite{Zhou2018}.}
    \label{fig:introduction}
\end{figure}

Generally speaking, PCQA methods can be divided into full-reference PCQA (FR-PCQA), reduced-reference (RR-PCQA), and no-reference PCQA (NR-PCQA) methods according to the involvement extent of the reference point clouds. However, the pristine reference point clouds are not always available in many practical situations, thus NR-PCQA has a wider range of applications. Hence, we focus on the NR-PCQA in this paper. Reviewing the development of NR-PCQA, NR-PCQA metrics are either based on the point cloud features extracted by statistical models \cite{zhang2021no} and end-to-end neural networks \cite{liu2022point} or based on the projected image features obtained via hand-crafted manners \cite{meynet2020pcqm,yang2020graphsim,alexiou2020pointssim} or 2D convolutional neural networks (CNN) \cite{liu2021pqa,fan2022no}. Such methods fail to jointly make use of the information from 3D point clouds along with 2D projections, thus resulting in unsatisfactory performance.

Therefore, to boost the performance of PCQA, we propose a multi-modal learning strategy for NR-PCQA, which extracts quality-aware features not only from the 3D point clouds but also from the 2D projections. There are two main reasons to adopt this strategy. First, point clouds can be perceived in both 2D/3D scenarios. We can view point clouds from 2D perspective via projecting them on the screen or directly watch point clouds in 3D format using the VR equipment. Thus multi-modal learning is able to cover more range of practical situations. Second, different types of distortions have divergent visual influence on different modalities. As shown in Fig. \ref{fig:introduction}, the structural damage and geometry down-sampling are more obvious in the point cloud modality while the image modality is more sensitive to texture distortions caused by color quantization and color noise. Moreover, it is easier to extract quality-aware semantic information from the image modality. Thus, the proposed multi-modal learning fashion can make up for the deficiencies and take advantage of both modalities.

 Further, considering that the local patterns such as smoothness and roughness are very important for quality assessment, we first propose to split the point cloud into sub-models rather than sampling points for analysis, which has been previously adopted for extracting the low-level pattern features of the point cloud \cite{chetouani2021deep,you2021patch}. The image projections are acquired by rendering the point clouds from random viewpoints with a fixed viewing distance to maintain the consistency of the texture scale. Then a point cloud encoder and an image encoder are used to encode the point clouds and projected images into quality-aware embeddings respectively, which are subsequently strengthened by symmetric cross-modality attention. Finally, the quality-aware embeddings are decoded into quality values with fully-connected layers. The main contributions of this paper are summarized as follows:

\begin{itemize}
    \item We propose a novel Multi-Modal learning framework for NR-PCQA (MM-PCQA) to interactively use the information from both the point cloud and image modalities. To the best of our knowledge, we are the first to introduce multi-modal learning into the PCQA field.
    \item To preserve the local patterns that are vital for visual quality, we propose to split the point cloud into sub-models rather than sampling points as the input of the point cloud encoder. To better incorporate the multi-modal features, we employ cross-modal attention to model the mutual relationship between the quality-aware features extracted from two modalities.
    \item Extensive experiments show that MM-PCQA achieves the best performance among the compared state-of-the-art methods (even including the FR-PCQA methods). The ablation studies reveal the contributions of different modalities, the patch-up strategy, and cross-modal attention, demonstrating the effectiveness of the proposed framework.
\end{itemize}

\begin{figure*}
    \centering
    \includegraphics[width = 17.5cm]{figs/framework.pdf}
    \caption{The framework of the proposed method.}
    \label{fig:framework}
\end{figure*}

\section{Related Work}
\subsection{Quality Assessment for Point Cloud}
In the early years of PCQA development, some simple point-based FR-PCQA methods are proposed by MPEG, such as p2point \cite{mekuria2016evaluation} and p2plane \cite{tian2017geometric}. To further deal with colored point clouds, point-based PSNR-yuv \cite{torlig2018novel} is carried out. Since the point-level difference is difficult to reflect complex distortions, many well-designed FR-PCQA metrics are proposed to employ structural features and have achieved considerable performance, which includes PCQM \cite{meynet2020pcqm}, GraphSIM \cite{yang2020graphsim}, PointSSIM \cite{alexiou2020pointssim}, etc.

To cover more range of practical applications, some NR-PCQA methods have been proposed as well.   Chetouani $et$  $al.$ \cite{chetouani2021deep} extract patch-wise hand-crafted features and use classical CNN models for quality regression. PQA-net \cite{liu2021pqa} utilizes multi-view projection for feature extraction. Zhang $et$  $al.$ \cite{zhang2021no} use several statistical distributions to estimate quality-aware parameters from the geometry and color attributes' distributions. Fan $et$  $al.$ \cite{fan2022no} infer the visual quality of point clouds via the captured video sequences. Liu $et$  $al.$ \cite{liu2022point} employ an end-to-end sparse CNN for quality prediction. Yang $et$  $al.$ \cite{yang2022no} further transfer the quality information from natural images to help understand the point cloud rendering images' quality via domain adaptation. The mentioned methods are all based on single-modal information, thus failing to jointly incorporate the multi-modal quality information.

\subsection{Multi-modal Learning for Point Cloud}
Many works \cite{arandjelovic2017look,radford2021learning,cheng2020look} have proven that multi-modal learning is capable of strengthening feature representation by actively relating the compositional information across different modalities such as image, text, and audio. Afterwards, various works utilize both point clouds and image data to improve the understanding of 3D vision, which are mostly targeted at 3D detection. Namely, AVOD \cite{ku2018joint} and MV3D \cite{chen2020object} make region proposals by mapping the LiDAR point clouds to bird's eye view. Then Qi $et$  $al.$ \cite{qi2018frustum} and Wang $et$  $al.$ \cite{wang2019frustum} attempt to localize the points by proposing 2D regions with 2D CNN object detector and then transforming the corresponding 2D pixels to 3D space.  Pointpainting \cite{vora2020pointpainting} projects the points into the image segmentation mask and detects 3D objects via LiDAR-based detector. Similarly, PI-RCNN \cite{xie2020pi} employs semantic feature maps and makes predictions through point-wise fusion. 3D-CVF   \cite{yoo20203d} transfers the camera-view features using auto-calibrated projection and fuses the multi-modal features with gated fusion networks. More recently, some works \cite{afham2022crosspoint,zhang2022cat} try to make use of multi-modal information in a self-supervised manner and transfer the learned representation to the downstream tasks.

\section{Proposed Method}
The framework overview is clearly exhibited in Fig. \ref{fig:framework}. The point clouds are first segmented into sub-models and put into the point cloud encoder $\theta_{P}$. The projected images are directly rendered from the colored point clouds and put into the image encoder $\theta_{I}$. Subsequently, the quality-aware encoder features are optimized with the assistance of symmetric cross-modality attention. Finally, the features are concatenated and decoded into the quality values via the quality regression.

\subsection{Preliminaries}
Suppose we have a colored point cloud $\mathbf{P} = \{g_{(i)} , c_{(i)} \}_{i=1}^{N}$, where $g_{(i)} \in \mathbb{R}^{1 \times 3} $ indicates the geometry coordinates, $c_{(i)} \in \mathbb{R}^{1 \times 3} $ represents the attached RGB color information, and $N$ stands for the number of points.  The point cloud modality $\mathbf{\hat{P}}$ is obtained by normalizing the original geometry coordinates and the image modality $\mathbf{I}$ is generated by rendering the colored point cloud $\mathbf{P}$ into 2D projections. Note that $\mathbf{\hat{P}}$ contains no color information.

\subsection{Point Cloud Feature Extraction}
It is natural to transfer mainstream 3D object detectors such as PointNet++ \cite{qi2017pointnet++} and DGCNN \cite{wang2019dynamic} to visual quality representation of point clouds. However, different from the point clouds used for classification and segmentation, the high-quality point clouds usually contain millions of dense points, which makes it difficult to extract features directly from the source points unless utilizing down-sampling. However, the common sampling methods are aimed at preserving semantic information whereas inevitably damaging the geometry patterns that are crucial for quality assessment. To avoid the geometry error caused by the down-sampling and preserve the smoothness and roughness of local patterns, we propose to split the point cloud into several local sub-models for geometric structure feature representation. Specifically, given a normalized point cloud $\mathbf{\hat{P}}$, we employ the farthest point sampling (FPS) to obtain $N_{\delta}$ anchor points $\{\delta_{m}\}_{m=1}^{N_{\delta}}$. For each anchor point, we utilize K nearest neighbor (KNN) algorithm to find $N_{s}$ neighboring points around the anchor point and form such points into a sub-model:

\begin{figure}[t]
    \centering
    \subfigure[Source]{\begin{minipage}[t]{0.31\linewidth}
                \centering
                \includegraphics[width = 2cm]{figs/pingpongbat.png}

                \end{minipage}}
    \subfigure[Anchor]{\begin{minipage}[t]{0.31\linewidth}
                \centering
                \includegraphics[width = 2.16cm]{figs/pingpongbat_fps.png}

                \end{minipage}}
    \subfigure[Sub-models]{\begin{minipage}[t]{0.31\linewidth}
                \centering
                \includegraphics[width = 2.32cm]{figs/pingpongbat_patch.png}

                \end{minipage}}
    \caption{An example of the patch-up process. (a) represents the source point cloud, (b) describes the anchor points after the farthest point sampling, and (c) shows the patch-up results after KNN operation. Color information is not included in the sub-models.}
    \label{fig:patch}
\end{figure}

\begin{equation}
   {\rm S} = \{\mathop{\boldsymbol{\rm{KNN}}}\limits_{k=N_{s}}(\delta_{m})\}_{m=1}^{N_{\delta}},
\end{equation}
where $\rm{S}$ is the set of sub-models, $\boldsymbol{\rm{KNN}}(\cdot)$ denotes the KNN operation and $\delta_{m}$ indicates the $m$-th farthest sampling point. We use $N_{\delta} \times N_{s} > N$ to ensure that the sub-models cover sufficient number of points in the source point cloud. An illustration of the patch-up process is exhibited in Fig. \ref{fig:patch}. It can be seen that the local sub-models are capable to preserve the local patterns.
We randomly select $N_{P}$ sub-models from the generated $N_{\delta}$ sub-models for geometric feature extraction. Then a point cloud feature encoder $\theta_{P}(\cdot)$ is employed to map the selected sub-models to quality-aware embedding space:

\begin{equation}
\begin{aligned}
   \rm{F_{P}} &= \{\theta_{P}(S_{l})\}_{l=1}^{N_{P}}, \\
   \widetilde{F}_{P} &= \frac{1}{N_{P}}\sum_{l = 1}^{N_{P}}F_{P}^{l},
\end{aligned}
\end{equation}
where $F_{P}^{l} \in \mathbb{R}^{1 \times C_{P}}$ indicates the quality-aware embedding for the $l$-th selected sub-model $S_{l}$, $C_{P}$ represents the number of output channels of the point cloud encoder $\theta_{P}(\cdot)$, and $\widetilde{F}_{P}  \in \mathbb{R}^{1 \times C_{P}}$ is the pooled results after average fusion.

\subsection{Image Feature Extraction}
Inspired by the success of PCQA methods via utilizing the image modality \cite{liu2021pqa,yang2020predicting}, we propose to use a 2D image encoder for feature extraction. $N_{I}$ image projections are rendered from the distorted colored point clouds from random viewpoints but with a fixed viewing distance to keep the texture consistent. Considering that the perceived texture visual quality varies at different scales \cite{wang2003multiscale,sun2021deep}, we propose to use hierarchical 2D CNN backbones to make use of multi-scale quality-aware features to better model the human perception of texture quality. Assume that the 2D CNN backbone has $j$ layers, the hierarchical features can be described as:
\begin{equation}
\begin{aligned}
     \theta_{I}(x) &= \alpha_{1}(x)\oplus\alpha_{2}(x) \cdots \oplus \alpha_{j}(x),\\
      \alpha_{k}(x) &= {\rm{GAP}}(L_{k}(x)), k \in \{1,\cdots, j\},
\end{aligned}
\end{equation}
where $\theta_{I}(\cdot)$ represents the hierarchical image encoder, $\oplus(\cdot)$ indicates the concatenation operation, $\rm{GAP}(\cdot)$ represents the global average pooling operation, $L_{k}(x)$ stands for the feature maps obtained from $k$-th layer, and $\alpha_{k}(x)$ denotes the average pooled features from $L_{k}(x)$.
Then we embed the rendered 2D images into quality-aware space with the hierarchical 2D image encoder:

\begin{equation}
\begin{aligned}
   F_{I} &= \{\theta_{I}(I_{t})\}_{t=1}^{N_{I}}, \\
   \widetilde{F}_{I} &= \frac{1}{N_{I}}\sum_{t = 1}^{N_{I}}F_{I}^{t},
\end{aligned}
\end{equation}
where $F_{I}^{t} \in \mathbb{R}^{1 \times C_{I}}$ denotes the quality-aware embedding for the $t$-th image projection $I_{t}$, $C_{I}$ represents the number of output channels of the 2D image encoder $\theta_{I}(\cdot)$, and $\widetilde{F}_{I}  \in \mathbb{R}^{1 \times C_{I}}$ is the pooled results after average fusion.

\subsection{Symmetric Cross-Modality Attention}
As shown in Fig \ref{fig:introduction}, the single modality may be incomplete to cover sufficient information for quality assessment, thus we propose a symmetric attention transformer block to investigate the interaction between the visual quality features gathered from the point cloud and image modalities. Given the intra-modal features $\widetilde{F}_{P}  \in \mathbb{R}^{1 \times C_{P}}$ and $\widetilde{F}_{I}  \in \mathbb{R}^{1 \times C_{I}}$ from the point clouds and images respectively, we adjust them to the same dimension with linear projection:
\begin{equation}
     \hat{F}_{P} = W_{P}\widetilde{F}_{P}, \quad \hat{F}_{I} = W_{I}\widetilde{F}_{I},
\end{equation}
where $\hat{F}_{P} \in \mathbb{R}^{1 \times C'}$ and $\hat{F}_{I} \in \mathbb{R}^{1 \times C'} $ are adjusted features, $W_{P}$ and $W_{I}$ are learnable linear mappings, and $C'$ is the number of adjusted channels. To further explore the discriminate components among the modalities, the multi-head attention module is utilized:

\begin{equation}
\begin{aligned}
     & \Gamma (Q,K,V) = (h_{1} \oplus h_{2} \cdots \oplus h_{n}) W, \\
     & h_{\mu} = \beta(QW_{\mu}^{Q},KW_{\mu}^{K},VW_{\mu}^{V})|_{\mu=1}^n,\\
     &\beta(Q,K,V) = {\rm softmax}(QK^{T}/\sqrt{d})V, \\
\end{aligned}
\end{equation}
where $\Gamma(\cdot)$ indicates the multi-head attention operation, $\beta(\cdot)$ represents the attention function, $h_{\mu}$ is the $\mu$-th head, and $W$, $W_{Q}$, $W_{K}$, $W_{V}$ are learnable linear mappings. As illustrated in Fig. \ref{fig:cross_modal}, both point-cloud-based and image-based quality-aware features participate in the attention learning of the other modality. The final quality embedding can be concatenated by the intra-modal features and the guided multi-modal features obtained by the symmetric cross-modality attention module:

\begin{equation}
    \hat{F}_{Q} = \hat{F}_{P} \oplus \hat{F}_{I} \oplus \Psi(\hat{F}_{P},\hat{F}_{I}),
\end{equation}
where $\oplus(\cdot)$ indicates the concatenation operation, $\Psi(\cdot)$ stands for the symmetric cross-modality attention operation, and $\hat{F}_{Q}$ represents the final quality-aware features ($\Psi(\hat{F}_{P},\hat{F}_{I}) \in \mathbb{R}^{1 \times 2C'}$, $\hat{F}_{Q} \in \mathbb{R}^{1 \times 4C'}$).

\begin{figure}[t]
    \centering
    \includegraphics[width=7cm]{figs/cross_modal.pdf}
    \caption{Illustration of the symmetric cross-modality attention module (SCMA). The point cloud embedding $\hat{F}_{P}$ is used to guide the attention learning of image embedding and the image embedding $\hat{F}_{I}$ is used to guide the attention learning of point cloud embedding respectively.}
    \label{fig:cross_modal}
\end{figure}

\subsection{Quality Regression \& Loss Function}
Following common practice, we simply use two-fold fully-connected layers to regress the quality features $\hat{F}_{Q}$ into predicted quality scores.
For the quality assessment tasks, we not only focus on the accuracy of the predicted quality values but also lay importance on the quality rankings \cite{fang2022perceptual,gu2019no}. Therefore, the loss function employed in this paper includes two parts: Mean Squared Error (MSE) and rank error. The MSE is utilized to keep the predicted values close to the quality labels, which can be derived as:

\begin{equation}
    L_{MSE} = \frac{1}{n} \sum_{\eta=1}^{n} (q_{\eta}-q_{\eta}')^2,
\end{equation}
where $q_{\eta}$ is the predicted quality scores, $q_{\eta}'$ is the quality labels of the point cloud, and $n$ is the size of the mini-batch. The rank loss can better assist the model to distinguish the quality difference when the point clouds have close quality labels. To this end, we use the differentiable rank function described in \cite{wen2021strong,sun2022deep} to approximate the rank loss:

\begin{equation}
\begin{aligned}
    L_{rank }^{i j}\!=\!\max\! &\left(0,\left|q_{i}-q_{j}\right|\!-\!e\left(q_{i}, q_{j}\right)\! \cdot\! \left({q}_{i}'-{q}_{j}'\right)\right), \\
    &e\left(q_{i}, q_{j}\right)=\left\{\begin{array}{r}
1, q_{i} \geq q_{j}, \\
-1, q_{i}<q_{j},
\end{array}\right.
\end{aligned}
\end{equation}
where $i$ and $j$ are the corresponding indexes for two point clouds in a mini-batch and the rank loss can be derived as:
\begin{equation}
    L_{r a n k}=\frac{1}{n^{2}} \sum_{i=1}^{n} \sum_{j=1}^{n} L_{r a n k}^{i j},
\end{equation}
Then the loss function can be calculated as the weighted sum of MSE loss and rank loss:
\begin{equation}
    Loss=\lambda_{1}L_{MSE}+\lambda_{2} L_{r a n k}
\end{equation}
where $\lambda_{1}$ and $\lambda_{2}$ are used to control the proportion of the MSE loss and the rank loss.

\section{Experiments}

\begin{table*}[th]
\centering
\caption{Performance comparison with state-of-the-art approaches on the SJTU-PCQA and WPC databases. 'P' and 'I' stand for the point cloud and image modalities respectively. Best in {\bf\textcolor{red}{red}} and second in {\bf\textcolor{blue}{blue}}. }
\begin{tabular}{c|l|c|cccc|cccc}
\toprule
\multirow{2}{*}{Type}&\multirow{2}{*}{Methods} &\multirow{2}{*}{Modal} & \multicolumn{4}{c|}{SJTU-PCQA} & \multicolumn{4}{c}{WPC} \\
\cline{4-11}
        & & & SRCC$\uparrow$       & PLCC$\uparrow$      & KRCC$\uparrow$     & RMSE$\downarrow$     & SRCC$\uparrow$      & PLCC$\uparrow$      & KRCC$\uparrow$       & RMSE$\downarrow$ \\ \hline
\multirow{8}{*}{FR}
& MSE-p2po & P & 0.72   & 0.81    & 0.56   & 1.36 & 0.45 & 0.48 &0.31 &19.89            \\
& HD-p2po & P & 0.71   & 0.77    & 0.54    & 1.44 & 0.27& 0.39 &0.19 &20.89             \\
& MSE-p2pl & P  & 0.62   & 0.59    & 0.48    & 2.28 & 0.32& 0.26 &0.22 &22.82            \\
& HD-p2pl & P  & 0.64   & 0.68    & 0.45    & 2.12 & 0.28& 0.27 &0.16 &21.98               \\
& PSNR-yuv & P  & 0.79   & 0.81    & 0.61    & 1.31 & 0.44 & 0.53 &0.31 &19.31                \\
  & PCQM    &P   &{0.86}   & {0.88}    & \bf\textcolor{blue}{0.70}    & 1.08 & \bf\textcolor{blue}{0.74}& \bf\textcolor{blue}{0.74} &\bf\textcolor{blue}{0.56} &{15.16}                 \\
 & GraphSIM &P  & \bf\textcolor{blue}{0.87}    & \bf\textcolor{blue}{0.89}    &  {0.69}  &  \bf\textcolor{blue}{1.03} & 0.58    & 0.61    & 0.41   & 17.19 \\
 & PointSSIM   &P   & 0.71  & 0.74  & 0.54 & 1.54 & 0.46 & 0.47 & 0.33 & 20.13   \\ \hline
 \multirow{1}{*}{RR} &
 PCMRR  &P   & 0.47  & 0.68  & 0.33 & 1.72 & 0.31 & 0.33 & 0.22 & 21.42  \\ \hline
 \multirow{4}{*}{NR}
 &BRISQUE &I  & 0.39    & 0.42  & 0.29 & 2.09 & 0.27  & 0.30  & 0.19 & 21.86 \\
 &NIQE &I & 0.13 &0.24 & 0.10 & 2.26 &0.11 &0.22 & 0.09 & 23.14 \\
 &IL-NIQE &I & 0.08 & 0.16 & 0.05 &2.33 &0.09 &0.14 & 0.08 &24.01\\
 &ResSCNN &P & 0.81 & 0.82 & 0.61 & 1.11 & 0.55 & 0.56 & 0.44 & 17.12 \\
 &PQA-net   &I   & 0.83   & 0.85    & 0.63 & {1.07}  & {0.70}    & {0.71}    & {0.49}   & \bf\textcolor{blue}{15.02}     \\
 &3D-NSS  &P    & 0.72 & 0.74  & 0.52 & 1.66  & 0.64 & 0.64  & 0.45 & 16.47  \\
 &MM-PCQA(Ours) &P+I    & \bf\textcolor{red}{0.91}   & \bf\textcolor{red}{0.92}   & \bf\textcolor[RGB]{242,12,22}{0.78} & \bf\textcolor{red}{0.77} &\bf\textcolor{red}{0.83}    & \bf\textcolor{red}{0.83}    & \bf\textcolor{red}{0.64}  & \bf\textcolor{red}{12.84} \\

                      \bottomrule
\end{tabular}

\label{tab:experiment}
\end{table*}

\subsection{Databases}
To test the performance of the proposed method, we employ the subjective point cloud assessment database (SJTU-PCQA) \cite{yang2020predicting} and the Waterloo point cloud assessment database (WPC) proposed by \cite{liu2022perceptual} for validation. The SJTU-PCQA database includes 9 reference point clouds and each point cloud is corrupted with seven types of distortions (compression, color noise, geometric shift, down-sampling, and three distortion combinations) under six strengths, which generates 378 = 9$\times$7$\times$6 distorted point clouds in total.
The WPC database contains 20 reference point clouds and augmented each point cloud with four types of distortions (down-sampling, Gaussian white noise, Geometry-based Point Cloud Compression (G-PCC), and Video-based Point Cloud Compression (V-PCC)), which generates 740 = 20$\times$37 distorted point clouds.

\subsection{Implementation Details}
The Adam optimizer \cite{kingma2014adam} is utilized with weight decay 1e-4 and the initial learning rate is set as 5e-5. The model is trained for 50 epochs by default. We set the point cloud sub-model size $N_{s}$ as 2048 and set $N_{\delta} = N/N_{s} + 1$. The projected images with the resolution of 1920$\times$1080$\times$3 are randomly cropped into image patches at the resolution of 224$\times$224$\times$3 as the inputs. During the experiment, 6 sub-models and 4 projected images are selected at random for each point cloud. The PointNet++ \cite{qi2017pointnet++} is utilized as the point cloud encoder and the hierarchical ResNet50 \cite{he2016deep} is used as the image encoder, where the ResNet50 is initialized with the pre-trained model on the ImageNet database \cite{deng2009imagenet}. The multi-head attention module employs 8 heads and the feed-forward dimension is set as 2048. The weights $\lambda_{1}$ and $\lambda_{2}$ for $L_{MSE}$ and $ L_{r a n k}$ are both set as 1.

Following the practices in \cite{chetouani2021deep,fan2022no}, the k-fold cross validation strategy is employed for the experiment to accurately estimate the performance of the proposed method. Specifically, for the SJTU-PCQA database, 1 group of point cloud is chosen as the testing set and the remaining 8 groups of point cloud are taken as the training set. Such procedure is repeated 9 times with each of the point cloud groups used exactly once as the testing set.  Similarly, we randomly split the WPC database into 5 folds and each fold consists of 4 groups of point clouds. Then a similar experiment procedure as discussed above is applied. In all, 9-fold cross validation is conducted on the SJTU-PCQA database and 5-fold cross validation is conducted on the WPC database. The average performance is recorded as the final results.
It's worth noting that there is no content overlap between the training and testing sets. We strictly retrain the available baselines with the same database split set up to keep the comparison fair. What's more, for the FR-PCQA and RR-PCQA methods that require no training, we simply validate them on the same testing sets and record the average performance.

\subsection{Competitors and Evaluation Criteria}
15 state-of-the-art quality assessment methods are selected for comparison, which consist of 8 FR-PCQA methods, 1 RR-PCQA method, and 6 NR-PCQA methods. The FR-PCQA methods include MSE-p2point (MSE-p2po) \cite{mekuria2016evaluation}, Hausdorff-p2point (HD-p2po) \cite{mekuria2016evaluation}, MSE-p2plane (MSE-p2pl) \cite{tian2017geometric}, Hausdorff-p2plane (HD-p2pl) \cite{tian2017geometric}, PSNR-yuv \cite{torlig2018novel}, PCQM \cite{meynet2020pcqm}, GraphSIM \cite{yang2020graphsim}, and PointSSIM \cite{alexiou2020pointssim}. The RR-PCQA method includes PCMRR \cite{viola2020pcmrr}. The NR-PCQA methods include BRISQUE \cite{mittal2012brisque}, NIQE \cite{mittal2012making}, IL-NIQE \cite{zhang2015feature}, ResSCNN \cite{liu2022point}, PQA-net \cite{liu2021pqa}, and 3D-NSS \cite{zhang2021no}. Note that BRISQUE, NIQE, IL-NIQE are image-based quality assessment metrics and are validated on the same projected images.
Furthermore, to deal with the scale differences between the predicted quality scores and the quality labels, a five-parameter logistic function is applied to map the predicted scores to subjective ratings, as suggested by \cite{fang2022perceptual,antkowiak2000final}.

Four mainstream evaluation criteria in the quality assessment field are utilized to compare the correlation between the predicted scores and MOSs, which include Spearman Rank Correlation Coefficient (SRCC), Kendall's Rank Correlation Coefficient (KRCC), Pearson Linear Correlation Coefficient (PLCC), Root Mean Squared Error (RMSE).
An excellent quality assessment model should obtain values of SRCC, KRCC, PLCC close to 1 and RMSE to 0.

\begin{table}[tb]
    \centering
    \caption{Contributions of the modalities, where `P' stands for only using point cloud features, `I' stands for using only image features, `P+I' represents using both modalities' features through simple concatenation, and `SCMA' indicates the symmetric cross-modality attention is used. Best in bold.}
    \begin{tabular}{c|cc|cc}
    \toprule
    \multirow{2}{*}{Modal}  & \multicolumn{2}{c|}{SJTU-PCQA} & \multicolumn{2}{c}{WPC} \\ \cline{2-5}
            & SRCC$\uparrow$    & PLCC$\uparrow$   & SRCC$\uparrow$   & PLCC$\uparrow$ \\ \hline
        P         &0.84 &0.89 & 0.52 & 0.55\\
        I         &0.88 &0.89 & 0.79 & 0.80\\
        P+I       &\bf{0.91} & 0.90& 0.81 & 0.82 \\
        P+I+SCMA       &\bf{0.91} & \bf{0.92} & \bf{0.83} & \bf{0.83} \\
    \bottomrule
    \end{tabular}
    \label{tab:modality}
\end{table}

\subsubsection{Performance Discussion}
The experimental results are listed in Table \ref{tab:experiment}, from which we can make several useful inspections: a) Our method MM-PCQA presents the best performance among the two databases and outperforms the compared NR-PCQA methods by a large margin. For example, MM-PCQA surpasses the second place NR-PCQA method PQA-net by 0.08 (0.91 vs. 0.83) on the SJTU-PCQA database and by 0.13 (0.83 vs. 0.70) on the WPC database in terms of SRCC. By jointly utilizing intra-modal and cross-modal features, MM-PCQA enforces the model to relate the compositional quality-aware patterns and mobilize under-complementary information between the image and point cloud modalities to optimize the quality representation, thus gaining higher performance; b) There is a significant performance drop from the SJTU-PCQA database to the WPC database since the WPC database contains more complex distortions, being more challenging for PCQA models. MM-PCQA achieves a relatively smaller performance drop than most compared methods. Particularly, the SRCC and PLCC values of MM-PCQA drop by 0.08 and 0.09 respectively. However, all compared PCQA methods except 3D-NSS experience a larger performance drop over 0.1 on both SRCC and PLCC values. Therefore, we can conclude that MM-PCQA gains more robustness over more complex distortions; c) The image-based handcrafted NR methods BRISQUE, NIQE, and IL-NIQE obtain the poorest performance. This is because such methods are carried out for evaluating the natural scene images, which have a huge gap from the projected images rendered from the point clouds.
In all, the overall experimental performance  firmly validates our motivation that multi-modal learning can help the model better understand the visual quality of point clouds.

\subsection{Contributions of the Modalities}
As described above, MM-PCQA jointly employs the features from the point cloud and image modalities and we hold the hypothesis that multi-modal learning helps the model to gain better quality representation than single-modality. Therefore, in this section, we conduct ablation studies to validate our hypothesis. The performance results are presented in Table \ref{tab:modality}. The performance of both single-modal-based model is inferior to the multi-modal-based model, which suggests that both point cloud and image features make contributions to the final results. After using SCMA module, the performance is further boosted, which validates the effectiveness of cross-modality attention.

\begin{table}[tb]
    \centering
    \caption{Contributions of the patch-up strategy, where `P' stands for only using point cloud features, `P+I' represents using both point cloud and image features, `FPS' indicates using farthest point sampling strategy, and `Patch-up' indicates using patch-up strategy. Best in bold.}
    \begin{tabular}{c|cc|cc}
    \toprule
    \multirow{2}{*}{Model}  & \multicolumn{2}{c|}{SJTU-PCQA} & \multicolumn{2}{c}{WPC} \\ \cline{2-5}
            & SRCC$\uparrow$    & PLCC$\uparrow$   & SRCC$\uparrow$   & PLCC$\uparrow$ \\ \hline
        P+FPS         &0.33 &0.34 & 0.12 & 0.15\\
        P+Patch-up         &0.84 &0.89 & 0.52 & 0.55\\
        P+I+FPS       &0.89 & 0.89& 0.78 & 0.79 \\
        P+I+Patch-up       &\bf{0.91} & \bf{0.92} & \bf{0.83} & \bf{0.83} \\
    \bottomrule
    \end{tabular}

    \label{tab:patch}
\end{table}

\begin{table}[t]
    \centering
    \caption{Cross-database evaluation, where SJTU$\rightarrow$WPC indicates the model is trained on the whole SJTU-PCQA database and validated with the default testing setup of the WPC database, and vice versa. Best in bold.}
    \begin{tabular}{c|cc|cc}
    \toprule
    \multirow{2}{*}{Model}  & \multicolumn{2}{c|}{SJTU$\rightarrow$WPC} & \multicolumn{2}{c}{WPC$\rightarrow$SJTU} \\ \cline{2-5}
            & SRCC$\uparrow$    & PLCC$\uparrow$   & SRCC$\uparrow$   & PLCC$\uparrow$ \\ \hline
        PQA-net         &0.17 &0.18 & 0.54 & 0.61\\
        3D-NSS         &0.15 &0.14 & 0.18 & 0.23\\
        MM-PCQA       &\bf{0.20} & \bf{0.30} & \bf{0.72} & \bf{0.79} \\
    \bottomrule
    \end{tabular}

    \label{tab:crossdatabase}
\end{table}

\begin{table}[t]
    \centering
    \caption{Performance results of the point cloud branch by varying the number of sub-models on the SJTU-PCQA and WPC databases.  Best in bold.}
    \begin{tabular}{c|cc|cc}
    \toprule
    Number of  & \multicolumn{2}{c|}{SJTU-PCQA} & \multicolumn{2}{c}{WPC} \\ \cline{2-5}
   3D sub-models         & SRCC$\uparrow$    & PLCC$\uparrow$   & SRCC$\uparrow$   & PLCC$\uparrow$ \\ \hline

        2         &0.74 &0.78 & 0.46 & 0.52\\
        4        &0.73 &0.82 & 0.45 & 0.49 \\
        6       &\bf{0.84} &\bf{0.89} & \bf{0.52} & \bf{0.55} \\
        8       &0.82 & \bf{0.89} & 0.50 & 0.54\\
    \bottomrule
    \end{tabular}
    \label{tab:ablation_patch}
\end{table}

\begin{table}[t]
\caption{Performance results of the image branch by varying the number of 2D projections on the SJTU-PCQA and WPC databases. Best in bold.}
    \centering
    \begin{tabular}{c|cc|cc}
    \toprule
    Number of  & \multicolumn{2}{c|}{SJTU-PCQA} & \multicolumn{2}{c}{WPC} \\ \cline{2-5}
   2D projections         & SRCC$\uparrow$    & PLCC$\uparrow$   & SRCC$\uparrow$   & PLCC$\uparrow$ \\ \hline

        2         &0.87 &0.87 & 0.75 & 0.76\\
        4        &\bf{0.88} & \bf{0.89}& \bf{0.79} & \bf{0.80} \\
        6       &\bf{0.88} &0.88 & \bf{0.79} & 0.78 \\
        8       &0.86 & 0.87& 0.78 & 0.79\\
    \bottomrule
    \end{tabular}
    \label{tab:ablation_2d}
\end{table}

\subsection{Ablation for the Patch-up Strategy}
To prove that the patch-up strategy is more suitable for PCQA, we present the performance of using farthest point sampling (FPS) strategy as well. To make the comparison even, 12,288 = 6$\times$2048 points (the same number of points as contained in the default 6 sub-models) are sampled for each point cloud and the results are shown in Table \ref{tab:patch}. It can be seen that the patch-up strategy greatly improves the performance when only point cloud features are used.
The reason is that the sampled points are not able to preserve the local patterns that are vital for quality assessment. Moreover, when the point cloud contains more points (even up to millions), the sampled points may not even maintain the main shape of the object unless greatly increasing the number of sampling points.

\subsection{Cross-database Evaluation}
The cross-database evaluation is further conducted to test the generalization ability of the proposed MM-PCQA and the experimental results are exhibited in Table \ref{tab:crossdatabase}.  From the table, we can find that the proposed MM-PCQA better generalizes learned feature representation from the WPC database and achieves much better performance than the most competitive NR-PCQA methods when validating on the SJTU-PCQA database. Although all the NR-PCQA models trained on the SJTU-PCQA database obtain poor performance, MM-PCQA still yields the best performance.

\subsection{Number of 2D Projections and 3D sub-models}
We try to investigate the contributions of the point cloud and image branch by varying the number of the 3D sub-models and input 2D projections. The performance results are exhibited in Table \ref{tab:ablation_patch} and Table \ref{tab:ablation_2d} . We can see that employing 6 sub-models and 4 projections yields the best performance for the point cloud and image branch respectively. With the number increasing, the features extracted from sub-models and projections may get redundant, thus resulting in the performance drop.

\subsection{Limitation}
From the above analysis, the major limitation of the proposed model lies in the cross-database (SJTU$\rightarrow$WPC) evaluation, mainly due to the following reasons: a) The number of training samples for the SJTU-PCQA database is half of the WPC database (378$\rightarrow$740), which means the SJTU-PCQA database contains less diverse content and distortion types. b) The distortion levels of the SJTU-PCQA database are relatively coarse-grained. Thus the quality representation learned from the SJTU-PCQA database is not effective on the WPC database which contains stimuli with fine-grained distortion levels. In all, due to the limited scale of current PCQA databases, the generalization performance of MM-PCQA is still unsatisfactory.

\section{Conclusion}
This paper proposes a novel multi-modal learning approach for no-reference point cloud quality assessment (MM-PCQA). MM-PCQA aims to acquire quality information across modalities and optimize the quality representation. In particular, the point clouds are patched up to preserve the important local geometric structure patterns. Then the projected images are employed to reflect the texture distortions. PointNet++ and hierarchical ResNet50 are utilized as the feature encoders. Symmetric cross-modal attention is further employed to make full use of the cross-modal information.  Experimental results show that MM-PCQA reaches a new state-of-the-art on the SJTU-PCQA and WPC database. Extensive ablation studies further demonstrate the potency of the proposed multi-modal learning framework.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Les amants d'un jour è una canzone scritta da Claude Delécluse e Michèle Senlis per il testo e da Marguerite Monnot per la musica, portata al successo in Francia da Édith Piaf nel 1956; la cantante la incise per il 45 giri Les Amants D'un Jour/Soudain Une Vallée (Columbia, SCRF 210), e nello stesso anno la incluse nell'EP Soudain, une vallée/L'homme à la moto/Avant nous/Les amants d'un jour (ESRF 1070).

Storia e testo
Il tema della canzone è quello di amore e morte. La storia si svolge all'interno di un albergo a ore, il narratore è un cameriere dell'albergo. Il cameriere, che lavorava seguendo la solita routine quotidiana, una mattina era stato colpito da una coppia di clienti che «puliti, educati, sembravano finti, sembravano proprio due santi dipinti!»

Così li aveva accomodati nella miglior stanza e gli aveva dato le lenzuola più nuove prima di salutarli con un sorriso. Tuttavia, più tardi scoprì la tragedia: i due giovani «se n'erano andati in silenzio perfetto, lasciando soltanto i due corpi nel letto.» Questo testo trattava dei temi insoliti in Italia in quegli anni: l'amore fuori del matrimonio, il suicidio. Per un certo tempo la censura provvedette ad oscurare il brano e fare in modo che non venisse trasmesso, ma nel '69 Pagani, dopo una lunga battaglia con la RAI, poté cantare in radio ed in televisione questa ed altre sue canzoni "proibite".

La versione in italiano
Herbert Pagani tradusse la canzone in italiano, lasciando inalterato il racconto, e la incise sul lato B del 45 giri Cento scalini/Albergo a ore nel 1969.

Altre versioni in italiano
La versione italiana ha goduto di una grande popolarità, ed è stata riproposta nel corso degli anni da:
 Gino Paoli (come 45 giri già nel 1969, poi nell'Lp Le due facce dell'amore, del 1971, e ancora in Il mio mestiere (1977)
 Milva, Canzoni di Edith Piaf, (1970)
 Ornella Vanoni, Ah! L'amore l'amore, quante cose fa fare l'amore! (1971), e Oggi le canto così, vol.1, (1979) e Ornella Vanoni Live@RTSI (2001)
 Marcella Bella, Tu non hai la più pallida idea dell'amore (1972)
 Luisella Guidetti, ...Stame tacà (1978)
 Ornella Vanoni e Gino Paoli, Insieme (1985), e VanoniPaoli Live (2005)
 Gianni Gori - L.P. Regalami un pensiero (1991)
 Paolo Rossi nel programma televisivo Su la testa! (1992), poi in Hammamet e altre storie  (1994)
 Patrizia Laquidara (2006)
 Lalo, Spaesando... Storie d'amore e libertà
 Macrobiotics, Balerasteppin (2011)
 Deborah Iurato Amici 13 (2014)
 Tish Amici 18 (2019)

Leopoldo Mastelloni ha fatto un libero adattamento in napoletano, ove i due amanti sono una coppia omosessuale, intitolato Hotel Syrena, nello spettacolo omonimo, e poi inciso col titolo originale "Les amants d'un jour" nella raccolta 87 amori, 2007. Anche nella versione di Paolo Rossi, la coppia è  omosessuale e uno dei due è un travestito.

Una parodia "Albergo a ore-Hotel scorretto" (gli amanti scappano dalla finestra senza pagare e rubando gli oggetti di valore) è stata cantata da Rosanna Ruffini,  nell'Lp - Sono nata con la camicia, del 1976.

Note

Singoli del 1969
Canzoni censurate

redpajama_set_name: RedPajamaWikipedia


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redpajama_set_name: RedPajamaCommonCrawl


As the markets once again approach historic highs—the overly exuberant tone, extreme complacency and weakness in the economic data, bring to mind Bob Farrell's 10 investment rules. These rules should be a staple for any long term successful investor. These rules are often quoted yet rarely heeded—just as they are now. Bob Farrell is a Wall Street veteran with over 50 years of experience in crafting his investing rules. Farrell obtained his masters degree from Columbia Business School and started as a technical analyst at Merrill Lynch in 1957. Even though Farrell studied fundamental analysis under Gramm and Dodd, he turned to technical analysis after realising there was more to stock prices than balance sheets and income statements. Farrell became a pioneer in sentiment studies and market psychology. His 10 rules on investing stem from personal experience with dull markets, bull markets, bear markets, crashes and bubbles. In short, Farrell has seen it all and lived to tell about it.
1. Markets tend to return to the mean (average price) over time.
Like a rubber band that has been stretched too far – it must be relaxed in order to be stretched again. This is exactly the same for stock prices which are anchored to their moving averages. Trends that get overextended in one direction, or another, always return to their long-term average. Even during a strong uptrend or strong downtrend, prices often move back (revert) to a long-term moving average. The chart below shows the S&P 500 with a 52-week simple moving average.
The bottom chart shows the percentage deviation of the current price of the market from the 52-week moving average. During bullish trending markets there are regular reversions to the mean which create buying opportunities. However, what is often not stated is that in order to take advantage of such buying opportunities profits should have been taken out of portfolios as deviations from the mean reached historical extremes. Conversely, in bearish trending markets, such reversions from extreme deviations should be used to sell stocks, raise cash and reduce portfolio risk rather than "panic sell" at market bottoms.
The dashed RED lines denote when the markets changed trends from positive to negative. This is the very essence of portfolio "risk" management.
2. Excesses in one direction will lead to an opposite excess in the other direction.
Markets that overshoot on the upside will also overshoot on the downside, kind of like a pendulum. The further it swings to one side, the further it rebounds to the other side. This is the extension of Rule #1 as it applies to longer term market cycles (cyclical markets).
While the chart above showed prices behave on a short term basis – on a longer term basis markets also respond to Newton's 3rd law of motion: "For every action there is an equal and opposite reaction." The first chart shows that cyclical markets reach extremes when more than 3-standard deviations above the 50-week moving average. For the first time since the lows of the market in 2009 this has now occurred. Notice that these excesses ARE NEVER worked off by just going sideways.
The second chart shows the price reversions of the S&P 500 on a long term basis and adjusted for inflation. Notice that when prices have historically reached extremes – the reversion in price is just as extreme. It is clear that the current reversion in the stock market is still underway from the 2000 peak.
3. There are no new eras – excesses are never permanent.
The reality is that excesses, such as we are seeing in the market now, can indeed go much further than logic would dictate. However, these excesses, as stated above, are never worked off simply by trading sideways. Corrections are always just as brutal as the advances were exhilarating. As the chart below shows when the markets broke out of their directional trends – the corrections came soon thereafter.
5. The public buys the most at the top and the least at the bottom.
The average individual investor is most bullish at market tops and most bearish at market bottoms. This is due to investor's emotional biases of "greed" when markets are rising and "fear" when markets are falling. Logic would dictate that the best time to invest is after a massive selloff – unfortunately this is exactly the opposite of what investors do.
The chart below shows the flow of money into equity based mutual funds.
6. Fear and greed are stronger than long-term resolve.
As stated in Rule $5 it is emotions that cloud your decisions and affect your long-term plan.
The composite index of bullish sentiment (an average of AAII and Investor's Intelligence surveys) shows that "greed" is beginning to reach levels where markets have generally reached intermediate term peaks.
Currently, those "people" are getting extremely greedy.
7. Markets are strongest when they are broad and weakest when they narrow to a handful of blue-chip names.
Breadth is important. A rally on narrow breadth indicates limited participation and the chances of failure are above average. The market cannot continue to rally with just a few large-caps (generals) leading the way. Small and mid-caps (troops) must also be on board to give the rally credibility. A rally that "lifts all boats" indicates far-reaching strength and increases the chances of further gains.
The chart above shows the ARMS Index which is a volume-based indicator that determines market strength and breadth by analysing the relationship between advancing and declining issues and their respective volume. It is normally used as a short term trading measure of market strength. However, for longer term periods the chart shows a weekly index smoothed with a 34-week average. Spikes in the index has generally coincided with near-term market peaks.
Bear markets often start with a sharp and swift decline. After this decline, there is an oversold bounce that retraces a portion of that decline. The longer term decline then continues, at a slower and more grinding pace, as the fundamentals deteriorate. Dow Theory suggests that bear markets consists of three down legs with reflexive rebounds in between.
The chart above shows the stages of the last two primary cyclical bear markets. There were plenty of opportunities to sell into counter-trend rallies during the decline and reduce risk exposure.
9. When all the experts and forecasts agree – something else is going to happen.
The point here is that as a contrarian investor, and along with several of the points already made within Farrell's rule set, excesses are built by everyone being on the same side of the trade. Ultimately, when the shift in sentiment occurs – the reversion is exacerbated by the stampede going in the opposite direction.
Psychologically, as the markets rise, investors begin to believe that they are "smart" because their portfolio is going up. In reality, it is primarily more a function of "luck" rather than "intelligence" that is driving their portfolio.
Investors behave much the same way as individuals who addicted to gambling. When they are winning they believe that their success is based on their skill. However, when they began to lose, they keep gambling thinking the next "hand" will be the one that gets them back on track. Eventually – they leave the table broke.
It is true that bull markets are more fun than bear markets. Bull markets elicit euphoria and feelings of psychological superiority. Bear markets bring fear, panic and depression.
What is interesting is that no matter how many times we continually repeat these "cycles" – as emotional human beings we always "hope" that somehow this "time will be different." Unfortunately, it never is and this time won't be either. The only questions are: when will the next bear market begin and will you be prepared for it?
Like all rules on Wall Street, Bob Farrell's rules are not meant has hard and fast rules. There are always exceptions to every rule and while history never repeats exactly it does often "rhyme" very closely.
Nevertheless, these rules will benefit investors by helping them to look beyond the emotions and the headlines. Being aware of sentiment can prevent selling near the bottom and buying near the top, which often goes against our instincts.
Regardless of how many times I discuss these issues, quote successful investors, or warn of the dangers – the response from both individuals and investment professionals is always the same.
No you're not. Yes, they do.
Individuals are long term investors only as long as the markets are rising. Despite endless warnings, repeated suggestions and outright recommendations – getting investors to sell, take profits and manage your portfolio risks is nearly a lost cause as long as the markets are rising. Unfortunately, by the time the fear, desperation or panic stages are reached it is far too late to act and I will only be able to say that I warned you.


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It's All About CEX! reveals how to generate positive experiences for your customers and employees, win their loyalty, and maximize your profits in the process.
It's All About CX is a masterfully-crafted, insightful resource for anyone who seeks to drive customer loyalty and referrals. Written from wisdom, experience, and a deep understanding of how to drive a branded customer experience. It's All About CX is refreshing, approachable, and empowering! It is filled with actionable tools that will drive sustainable customer success!
The secret to a great customer experience (CX) is a great employee experience (EX). In other words, what's happening on the inside of an organization is going to be felt on the outside by the customer. Jason Bradshaw knows this first hand, as that was one of his responsibilities in his role as an executive at a major brand, and now he teaches you. Don't just read this book. Use this book. Execute the ideas on these pages you will positively impact the experience you create for both your employees and your customers.
It's simple: customer experience matters now, more then ever, and in this book, Jason will help you accelerate improvements.
"Bradshaw's book absolutely nails it! He takes us on a journey that explains why both customer and employee experience matters so significantly — and he provides real world strategies you can implement today to create lifelong customers and advocates. Apply his advice and become iconic for your outstanding customer experiences — this is a MUST read!"
JASON BRADSHAW created his first business at fourteen, selling telecommunications and computer equipment in the Australian regional city of Toowoomba. In the lead up to this, he was inspired by books like The Pursuit of Wow!by Tom Peters, which opened his eyes to the power of customer and employee experience.
Jason has tested and implemented strategies for improving experience in a variety of sectors, including telecommunications, retail, media, finance, automotive, health, and not-for-profit. He has worked with companies including Telstra, Target Australia, Fairfax Media, New South Wales Government, SingTel Optus, and Volkswagen Group Australia, and he sat on the boards of ACON Health Limited and Oz ShowBiz Cares / Equity Fights AIDS.
A cornerstone of Jason's career has been an unwavering commitment to improving the lives of customers and employees. During his time in the telecommunications industry, he was able to implement ideas for improving customer experience in a number of verticals by over 100 percent. In retail and media, his initiatives to improve efficiencies by focusing on customer and employee experience saved tens of millions of dollars in a matter of months and improved employee retention by double digits year on year.