mlsanityData to 7 Oct 2026

Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversion

arXiv 2404.07545 ↗First posted 11 Apr 2024cs.CVcited by 4

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Abstract (arXiv)

Accurate and dense depth estimation with stereo cameras and LiDAR is an important task for automatic driving and robotic perception. While sparse hints from LiDAR points have improved cost aggregation in stereo matching, their effectiveness is limited by the low density and non-uniform distribution. To address this issue, we propose a novel stereo-LiDAR depth estimation network with Semi-Dense hint Guidance, named SDG-Depth. Our network includes a deformable propagation module for generating a semi-dense hint map and a confidence map by propagating sparse hints using a learned deformable window. These maps then guide cost aggregation in stereo matching. To reduce the triangulation error in depth recovery from disparity, especially in distant regions, we introduce a disparity-depth conversion module. Our method is both accurate and efficient. The experimental results on benchmark tests show its superior performance. Our code is available at https://github.com/SJTU-ViSYS/SDG-Depth.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 33 of its 43 references that are in the map. First posted on arXiv 11 Apr 2024.

Builds on

33 of 43 references.

References · 33

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21 more
  1. 2020AANet: Adaptive Aggregation Network for Efficient Stereo Matchingcited by 202
  2. 2020Non-Local Spatial Propagation Network for Depth Completioncited by 194
  3. 2019Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Drivingcited by 188
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  17. 2021Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimationcited by 15
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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Failcited by 45
  2. 2024A Survey on Deep Stereo Matching in the Twentiescited by 23
  3. 2025Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimationcited by 9
  4. 2025Preserving Guidance in Cost-Volume Retrieval under Extreme LiDAR Sparsity in Iterative Stereocited by 0