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license: cc-by-nc-sa-4.0
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# ToF-360 Dataset  (test)

![Figure showing multiple modalities](assets/figure/figure_1.png?raw=true)

## Overview  
The ToF-360 dataset consists of spherical RGB-D images with instance-level semantic and room ayout annotations, which include 4 unique scenes. It contains 179 equirectangular RGB images along with the corresponding depths, surface normals, XYZ images, and HHA images, labeled with building-defining object categories and image based layout boundaries (ceiling-wall, wall-floor). The dataset enables development of scene understanding tasks based on single-shot reconstruction without the need for global alignment in indoor spaces.


## Dataset Modalities  
Each scenes in the dataset has its own folder in the dataset. All the modalities and metadata for each area are contained in that folder as `<scene>/<modality>`.

**HHA images:**  
We followed [[Depth2HHA-python]](https://github.com/charlesCXK/Depth2HHA-python) to create it.

**RGB images:**  
RGB images contain equirectangular 24-bit color images converted from raw dual fisheye image. 

**Manhattan aligned RGB images:**  
We followed [[LGT-Net]](https://github.com/zhigangjiang/LGT-Net) to create Manhattan aligned RGB images.

**XYZ images:**  
XYZ images are saved as `.npy` binary file format in NumPy. It contains pixel-aligned set of data points in space with a sensitivity of mm. It must be the size of (Height, Width, 3[xyz]).

**Annotation:**  

**depth:**  
Depth images are stored as 16-bit PNGs having a maximum depth of 128m and a sensitivity of 1/512m. Missing values are encoded with the value 0. Note that while depth is defined as the distance from the point-center of the camera in the panoramics. 

**Room layout annotation:**  
Room layout annotations are stored as same json format as [PanoAnnotator](https://github.com/SunDaDenny/PanoAnnotator). Please refer to this repo for more details.

**Normal images:**  
Normals are 127.5-centered per-channel surface normal images. The normal vector is saved as 24-bit RGB PNGs where Red is the horizontal value (more red to the right), Green is vertical (more green downwards), and Blue is towards the camera. It is computed by [normal estimation function](https://www.open3d.org/docs/0.7.0/python_api/open3d.geometry.estimate_normals.html) in [Open3D](https://github.com/isl-org/Open3D). The tool for creating normal images from 3D is located in the `assets/compute_normal.py`

## Tools  
This repository provides some basic tools for interacting with the dataset and how to get preprocessed data. The tools are located in the `assets/preprocessing` folder.

## Evaluation  
**Semantic segmentation (image-based):**  


**Semantic segmentation (pointcloud-based):**  


**Layout estimation:**  


## Citations  
Coming soon...