1 citations · 1 across the 4 of their papers we have counts for
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Propagating Sparse Depth via Depth Foundation Model for Out-of-Distribution Depth Completion
Shenglun Chen, Xinzhu Ma, Hong Zhang +2
Depth completion is a pivotal challenge in computer vision, aiming at reconstructing the dense depth map from a sparse one, typically with a paired RGB image. Existing learning bas…
Learning Pixel-wise Continuous Depth Representation via Clustering for Depth Completion
Chen Shenglun, Zhang Hong, Ma XinZhu +2
Depth completion is a long-standing challenge in computer vision, where classification-based methods have made tremendous progress in recent years. However, most existing classific…
Full Matching on Low Resolution for Disparity Estimation
Hong Zhang, Shenglun Chen, Zhihui Wang +2
A Multistage Full Matching disparity estimation scheme (MFM) is proposed in this work. We demonstrate that decouple all similarity scores directly from the low-resolution 4D volume…
Direct Depth Learning Network for Stereo Matching
Hong Zhang, Haojie Li, Shenglun Chen +4
Being a crucial task of autonomous driving, Stereo matching has made great progress in recent years. Existing stereo matching methods estimate disparity instead of depth. They trea…