mlsanityData to 14 Oct 2026

Robust and accurate depth estimation by fusing LiDAR and Stereo

arXiv 2207.06139 ↗First posted 13 Jul 2022cs.CV cs.ROcited by 0

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Abstract (arXiv)

Depth estimation is one of the key technologies in some fields such as autonomous driving and robot navigation. However, the traditional method of using a single sensor is inevitably limited by the performance of the sensor. Therefore, a precision and robust method for fusing the LiDAR and stereo cameras is proposed. This method fully combines the advantages of the LiDAR and stereo camera, which can retain the advantages of the high precision of the LiDAR and the high resolution of images respectively. Compared with the traditional stereo matching method, the texture of the object and lighting conditions have less influence on the algorithm. Firstly, the depth of the LiDAR data is converted to the disparity of the stereo camera. Because the density of the LiDAR data is relatively sparse on the y-axis, the converted disparity map is up-sampled using the interpolation method. Secondly, in order to make full use of the precise disparity map, the disparity map and stereo matching are fused to propagate the accurate disparity. Finally, the disparity map is converted to the depth map. Moreover, the converted disparity map can also increase the speed of the algorithm. We evaluate the proposed pipeline on the KITTI benchmark. The experiment demonstrates that our algorithm has higher accuracy than several classic methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 41 references that are in the map. First posted on arXiv 13 Jul 2022.

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16 of 41 references.

References · 16

  1. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  2. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  3. 2017Sparsity Invariant CNNscited by 326
  4. 2015Stereo Matching by Training a Convolutional Neural Network to Compare Image Patchescited by 151
  5. 2018In Defense of Classical Image Processing: Fast Depth Completion on the CPUcited by 96
  6. 2017Intel RealSense Stereoscopic Depth Camerascited by 83
  7. 2020Adaptive Context-Aware Multi-Modal Network for Depth Completioncited by 69
  8. 2022Multi-Frame Self-Supervised Depth with Transformerscited by 64
  9. 2021Unsupervised Depth Completion with Calibrated Backprojection Layerscited by 61
  10. 20193D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalizationcited by 17
  11. 2021Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimationcited by 15
  12. 2019LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagerycited by 13
4 more
  1. 2016High-resolution LIDAR-based Depth Mapping using Bilateral Filtercited by 7
  2. 2021LiDARTouch: Monocular metric depth estimation with a few-beam LiDARcited by 7
  3. 2021SGTBN: Generating Dense Depth Maps from Single-Line LiDARcited by 6
  4. 2018Real Time Dense Depth Estimation by Fusing Stereo with Sparse Depth Measurementscited by 5

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