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LiDAR-Stereo: Dense Depth Estimation from Sparse LiDAR and Stereo Images

Obtaining accurate depth information is vital for real world 3D perception, and a combination of different sensors is frequently used for better performance. In this paper, we propose an approach for high-precision depth estimation by jointly utilizing information acquired from stereo cameras and LiDAR sensors. More specifically, we design novel convolutional neural network (CNN) architecture with two novel units: monocular completion unit and stereo fusion unit, and the complementary properties of sparse LiDAR and dense stereo images are fully explored in a boosting manner. Extensive experiments on both synthetic and real datasets have shown that the proposed approach achieves significant improvements over state-of-the-art techniques in terms of both subjective and objective evaluations.