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.
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 @cite_11 . Other methods along this line include @cite_5 @cite_33 @cite_4 @cite_31 @cite_17 , while Zhang and Funkhouser @cite_28 used a neural network to predict normals and occlusion boundaries to aid inpainting.
@cite_4 @cite_33 @cite_28 @cite_17 @cite_5 @cite_31 @cite_11
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We present a deep model that can accurately produce dense depth maps given an RGB image with known depth at a very sparse set of pixels. The model works simultaneously for both indoor outdoor scenes and produces state-of-the-art dense depth maps at nearly real-time speeds on both the NYUv2 and KITTI datasets. We surpass the state-of-the-art for monocular depth estimation even with depth values for only 1 out of every ( 10000 ) image pixels, and we outperform other sparse-to-dense depth methods at all sparsity levels. With depth values for (1 256 ) of the image pixels, we achieve a mean error of less than (1 ) of actual depth on indoor scenes, comparable to the performance of consumer-grade depth sensor hardware. Our experiments demonstrate that it would indeed be possible to efficiently transform sparse depth measurements obtained using e.g. lower-power depth sensors or SLAM systems into high-quality dense depth maps. We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image. Since depth estimation from monocular images alone is inherently ambiguous and unreliable, to attain a higher level of robustness and accuracy, we introduce additional sparse depth samples, which are either acquired with a low-resolution depth sensor or computed via visual Simultaneous Localization and Mapping (SLAM) algorithms. We propose the use of a single deep regression network to learn directly from the RGB-D raw data, and explore the impact of number of depth samples on prediction accuracy. Our experiments show that, compared to using only RGB images, the addition of 100 spatially random depth samples reduces the prediction root-mean-square error by 50 on the NYU-Depth-v2 indoor dataset. It also boosts the percentage of reliable prediction from 59 to 92 on the KITTI dataset. We demonstrate two applications of the proposed algorithm: a plug-in module in SLAM to convert sparse maps to dense maps, and super-resolution for LiDARs. Software and video demonstration are publicly available. Depth estimation has achieved considerable success with the development of the depth sensor devices and deep learning method. However, depth estimation from monocular RGB-based image will increase ambiguity and is prone to error. In this paper, we present a novel approach to produce dense depth map from a single image coupled with coarse point-cloud samples. Our approach learns to fit the distribution of the depth map from source data using conditional adversarial networks and convert the sparse point clouds to dense maps. Our experiments show that the use of the conditional adversarial networks can add full image information to the predicted depth maps and the effectiveness of our approach to predict depth in NYU-Depth-v2 indoor dataset. We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image. Since depth estimation from monocular images alone is inherently ambiguous and unreliable, to attain a higher level of robustness and accuracy, we introduce additional sparse depth samples, which are either acquired with a low-resolution depth sensor or computed via visual Simultaneous Localization and Mapping (SLAM) algorithms. We propose the use of a single deep regression network to learn directly from the RGB-D raw data, and explore the impact of number of depth samples on prediction accuracy. Our experiments show that, compared to using only RGB images, the addition of 100 spatially random depth samples reduces the prediction root-mean-square error by 50 on the NYU-Depth-v2 indoor dataset. It also boosts the percentage of reliable prediction from 59 to 92 on the KITTI dataset. We demonstrate two applications of the proposed algorithm: a plug-in module in SLAM to convert sparse maps to dense maps, and super-resolution for LiDARs. Software22https: github.com fangchangma sparse-to-dense and video demonstration33https: www.youtube.com watch?v=vNIIT_M7×7Y are publicly available.