mlsanityData to 7 Oct 2026

RePLAy: Remove Projective LiDAR Depthmap Artifacts via Exploiting Epipolar Geometry

arXiv 2407.19154 ↗First posted 27 Jul 2024cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

3D sensing is a fundamental task for Autonomous Vehicles. Its deployment often relies on aligned RGB cameras and LiDAR. Despite meticulous synchronization and calibration, systematic misalignment persists in LiDAR projected depthmap. This is due to the physical baseline distance between the two sensors. The artifact is often reflected as background LiDAR incorrectly projected onto the foreground, such as cars and pedestrians. The KITTI dataset uses stereo cameras as a heuristic solution to remove artifacts. However most AV datasets, including nuScenes, Waymo, and DDAD, lack stereo images, making the KITTI solution inapplicable. We propose RePLAy, a parameter-free analytical solution to remove the projective artifacts. We construct a binocular vision system between a hypothesized virtual LiDAR camera and the RGB camera. We then remove the projective artifacts by determining the epipolar occlusion with the proposed analytical solution. We show unanimous improvement in the State-of-The-Art (SoTA) monocular depth estimators and 3D object detectors with the artifacts-free depthmaps.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 37 of its 71 references that are in the map. First posted on arXiv 27 Jul 2024.

Builds on

37 of 71 references.

References · 37

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  2. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  3. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  4. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 701
  5. 2023ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depthcited by 646
  6. 2020AdaBins: Depth Estimation using Adaptive Binscited by 596
  7. 2015Holistically-Nested Edge Detectioncited by 590
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  10. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 379
  11. 2021Categorical Depth Distribution Network for Monocular 3D Object Detectioncited by 326
  12. 2017Sparsity Invariant CNNscited by 325
25 more
  1. 2019M3D-RPN: Monocular 3D Region Proposal Network for Object Detectioncited by 251
  2. 2021Is Pseudo-Lidar needed for Monocular 3D Object detection?cited by 248
  3. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
  4. 2019Learning Depth-Guided Convolutions for Monocular 3D Object Detectioncited by 169
  5. 2020MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationshipscited by 153
  6. 2022NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimationcited by 151
  7. 2021Delving into Localization Errors for Monocular 3D Object Detectioncited by 138
  8. 2022DeepInteraction: 3D Object Detection via Modality Interactioncited by 125
  9. 2020Kinematic 3D Object Detection in Monocular Videocited by 116
  10. 2022MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformercited by 108
  11. 2020Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systemscited by 100
  12. 2018In Defense of Classical Image Processing: Fast Depth Completion on the CPUcited by 96
  13. 2021MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagationcited by 82
  14. 2023Virtual Sparse Convolution for Multimodal 3D Object Detectioncited by 81
  15. 2022SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimationcited by 76
  16. 2020The Edge of Depth: Explicit Constraints between Segmentation and Depthcited by 72
  17. 2023iDisc: Internal Discretization for Monocular Depth Estimationcited by 71
  18. 2021GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detectioncited by 66
  19. 2022Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detectioncited by 49
  20. 2020Depth Estimation from Monocular Images and Sparse Radar Datacited by 44
  21. 2021The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detectioncited by 34
  22. 2021Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Imagecited by 20
  23. 2024SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objectscited by 12
  24. 2022Present and Future of SLAM in Extreme Underground Environmentscited by 9
  25. 2020Making Mobile Augmented Reality Applications Accessiblecited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Detailscited by 250
  2. 2025Unleashing the Power of Chain-of-Prediction for Monocular 3D Object Detectioncited by 8
  3. 2026UniDAC: Universal Metric Depth Estimation for Any Cameracited by 7
  4. 2026FounRef: Robust, Structure-Preserving, and Fast Metric Refinement of Frozen Monocular Foundation Priors with Sparse Anchorscited by 0