You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Despite the remarkable success of modern monocular depth estimation methods, the accuracy achievable from a single image is limited, making it is practically useful to incorporate other sources of depth information. Currently, depth estimation from different combinations of sources are treated as different applications, and solved via separate networks trained to use the set of available sources as input for each application. In this paper, we propose a common versatile model that outputs a probability distribution over scene depth given an input color image, as a sample approximation using outputs from a conditional GAN. This distributional output is useful even in the monocular setting, and can be used to estimate depth, pairwise ordering, etc. More importantly, these outputs can be combined with a variety of other depth cues---such as user guidance and partial measurements---for use in different application settings, without retraining. We demonstrate the efficacy of our approach through experiments on the NYUv2 dataset for a number of tasks, and find that our results from a common model, trained only once, are comparable to those from state-of-the-art methods with separate task-specific models.

Cited Abstract(s):
[1]: In this paper, we propose to conduct inpainting and upsampling for defective depth maps when aligned color images are given. These tasks are referred to as the guided depth enhancement problem. We formulate the problem based on the heat diffusion framework. The pixels with known depth values are treated as the heat sources and the depth enhancement is performed via diffusing the depth from these sources to unknown regions. The diffusion conductivity is designed in terms of the guidance color image so that a linear anisotropic diffusion problem is formed. We further cast the steady state problem of this diffusion into the famous random walk model, by which the enhancement is achieved efficiently by solving a sparse linear system. The proposed algorithm is quantitatively evaluated on the Middlebury stereo dataset and is applied to inpaint Kinect data and upsample Lidar's range data. Comparisons to the commonly used bilateral filter and Markov Random Field based methods are also presented, showing that our algorithm is competent.

[2]: 

[3]: The goal of our work is to complete the depth channel of an RGB-D image. Commodity-grade depth cameras often fail to sense depth for shiny, bright, transparent, and distant surfaces. To address this problem, we train a deep network that takes an RGB image as input and predicts dense surface normals and occlusion boundaries. Those predictions are then combined with raw depth observations provided by the RGB-D camera to solve for depths for all pixels, including those missing in the original observation. This method was chosen over others (e.g., inpainting depths directly) as the result of extensive experiments with a new depth completion benchmark dataset, where holes are filled in training data through the rendering of surface reconstructions created from multiview RGB-D scans. Experiments with different network inputs, depth representations, loss functions, optimization methods, inpainting methods, and deep depth estimation networks show that our proposed approach provides better depth completions than these alternatives.

[4]: We introduce a technique to fill large holes in LiDAR data sets. We combine concepts from patch-based image inpainting and gradient-domain image editing to simultaneously fill both texture and structure in a LiDAR scan. We discuss the problems with directly inpainting a depth image, and present a solution to this problem based on inpainting the depth gradients. Once the inpainted depth gradients are obtained, we use an image reconstruction technique to obtain the final 3D scene structure. We present several real-world examples of this technique with excellent results.

[5]: Many 3D reconstruction methods produce incomplete depth maps. Depth map inpainting can generate visually plausible structures for the missing areas. We present an inpainting method that encourages flat surfaces without favouring fronto-parallel planes. Moreover, it uses a color image to guide the inpainting and align color and depth edges. We implement the algorithm efficiently through graph-cuts. We compare the performance of our method with another inpainting approach used for large datasets and we show the results using several datasets. The depths inpainted with our method are visually plausible and of higher quality.

[6]: This paper describes a depth image enhancement method for consumer RGB-D cameras. Most existing methods use the pixel-coordinates of the aligned color image. Because the image plane generally has no relationship to the measured surfaces, the global coordinate system is not suitable to handle their local geometries. To improve enhancement accuracy, we use local tangent planes as local coordinates for the measured surfaces. Our method is composed of two steps, a calculation of the local tangents and surface reconstruction. To accurately estimate the local tangents, we propose a color heuristic calculation and an orientation correction using their positional relationships. Additionally, we propose a surface reconstruction method by ray-tracing to local tangents. In our method, accurate depth image enhancement is achieved by using the local geometries approximated by the local tangents. We demonstrate the effectiveness of our method using synthetic and real sensor data. Our method has a high completion rate and achieves the lowest errors in noisy cases when compared with existing techniques.

[7]: Colorization is a computer-assisted process of adding color to a monochrome image or movie. The process typically involves segmenting images into regions and tracking these regions across image sequences. Neither of these tasks can be performed reliably in practice; consequently, colorization requires considerable user intervention and remains a tedious, time-consuming, and expensive task.In this paper we present a simple colorization method that requires neither precise image segmentation, nor accurate region tracking. Our method is based on a simple premise; neighboring pixels in space-time that have similar intensities should have similar colors. We formalize this premise using a quadratic cost function and obtain an optimization problem that can be solved efficiently using standard techniques. In our approach an artist only needs to annotate the image with a few color scribbles, and the indicated colors are automatically propagated in both space and time to produce a fully colorized image or sequence. We demonstrate that high quality colorizations of stills and movie clips may be obtained from a relatively modest amount of user input.
Since making dense depth measurements is slow and expensive, it is useful to be able to recover a high quality dense depth map from a small number of direct measurements, by exploiting monocular cues from a color image. A popular way of combining color information with partial measurements is by requiring color and depth edges to co-occur. This approach is often successful for depth inpainting'', i.e., filling in gaps of missing measurements in a depth map (common in measurements from structured light sensors). A notable and commonly-used example is the colorization method of Levin al [7] . Other methods along this line include [5] [2] [1] [6] [4] , while Zhang and Funkhouser [3] used a neural network to predict normals and occlusion boundaries to aid inpainting.