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Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera
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Depth completion, the technique of estimating a dense depth image from sparse depth measurements, has a variety of applications in robotics and autonomous driving. However, depth completion faces 3 main challenges: the irregularly spaced pattern in the sparse depth input, the difficulty in handling multiple sensor modalities (when color images are available), as well as the lack of dense, pixel-level ground truth depth labels. In this work, we address all these challenges. Specifically, we develop a deep regression model to learn a direct mapping from sparse depth (and color images) to dense depth. We also propose a self-supervised training framework that requires only sequences of color and sparse depth images, without the need for dense depth labels. Our experiments demonstrate that our network, when trained with semi-dense annotations, attains state-of-the- art accuracy and is the winning approach on the KITTI depth completion benchmark at the time of submission. Furthermore, the self-supervised framework outperforms a number of existing solutions trained with semi- dense annotations.
Forward citations
Cited by 4 Pith papers
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Indoor Depth Completion with Boundary Consistency and Self-Attention
A self-attention depth completion network with a Sobel-supervised boundary consistency loss reports state-of-the-art results on Matterport3D.
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Image-Guided Depth Sampling and Reconstruction
An RGB-superpixel-guided depth sampling and reconstruction method with one sample per segment plus bilateral filtering outperforms random and grid sampling on indoor and outdoor benchmarks.
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Learning Guided Convolutional Network for Depth Completion
A guided convolutional network with factorized, content-dependent spatially-variant kernels achieves state-of-the-art depth completion on KITTI and NYUv2.
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To complete or to estimate, that is the question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation
A joint multi-task network performs monocular depth estimation and sparse depth completion with a shared two-stage architecture, reporting competitive-to-superior numbers on KITTI, but the evaluation omits a stronger ...
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