Leveraging Sparse LiDAR for RAFT-Stereo: A Depth Pre-Fill Perspective

• URL: http://arxiv.org/abs/2507.19738v1
• Date: Sat, 26 Jul 2025 02:03:02 GMT
• Title: Leveraging Sparse LiDAR for RAFT-Stereo: A Depth Pre-Fill Perspective
• Authors: Jinsu Yoo, Sooyoung Jeon, Zanming Huang, Tai-Yu Pan, Wei-Lun Chao,
• Abstract summary: We investigate LiDAR guidance within the RAFT-Stereo framework.<n>We aim to improve stereo matching accuracy by injecting precise LiDAR depth into the initial disparity map.<n>We find that the effectiveness of LiDAR guidance drastically degrades when the LiDAR points become sparse.
• Score: 23.15129268391347
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We investigate LiDAR guidance within the RAFT-Stereo framework, aiming to improve stereo matching accuracy by injecting precise LiDAR depth into the initial disparity map. We find that the effectiveness of LiDAR guidance drastically degrades when the LiDAR points become sparse (e.g., a few hundred points per frame), and we offer a novel explanation from a signal processing perspective. This insight leads to a surprisingly simple solution that enables LiDAR-guided RAFT-Stereo to thrive: pre-filling the sparse initial disparity map with interpolation. Interestingly, we find that pre-filling is also effective when injecting LiDAR depth into image features via early fusion, but for a fundamentally different reason, necessitating a distinct pre-filling approach. By combining both solutions, the proposed Guided RAFT-Stereo (GRAFT-Stereo) significantly outperforms existing LiDAR-guided methods under sparse LiDAR conditions across various datasets. We hope this study inspires more effective LiDAR-guided stereo methods.

Related papers

• LiDAR-GS:Real-time LiDAR Re-Simulation using Gaussian Splatting [50.808933338389686]
  We present LiDAR-GS, a real-time, high-fidelity re-simulation of LiDAR scans in public urban road scenes.<n>The method achieves state-of-the-art results in both rendering frame rate and quality on publically available large scene datasets.
  arXiv  Detail & Related papers  (2024-10-07T15:07:56Z)
• UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generation [51.443788294845845]
  We present UltraLiDAR, a data-driven framework for scene-level LiDAR completion, LiDAR generation, and LiDAR manipulation. We show that by aligning the representation of a sparse point cloud to that of a dense point cloud, we can densify the sparse point clouds. By learning a prior over the discrete codebook, we can generate diverse, realistic LiDAR point clouds for self-driving.
  arXiv  Detail & Related papers  (2023-11-02T17:57:03Z)
• Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory [20.452961476175812]
  This letter explores the capability of LiDAR-only odometry through a continuous-time perspective. Our proposed Traj-LO approach tries to recover the spatial-temporal consistent movement of LiDAR. Our implementation is open-sourced on GitHub.
  arXiv  Detail & Related papers  (2023-09-25T03:05:06Z)
• Boosting 3D Object Detection by Simulating Multimodality on Point Clouds [51.87740119160152]
  This paper presents a new approach to boost a single-modality (LiDAR) 3D object detector by teaching it to simulate features and responses that follow a multi-modality (LiDAR-image) detector. The approach needs LiDAR-image data only when training the single-modality detector, and once well-trained, it only needs LiDAR data at inference. Experimental results on the nuScenes dataset show that our approach outperforms all SOTA LiDAR-only 3D detectors.
  arXiv  Detail & Related papers  (2022-06-30T01:44:30Z)
• LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection [96.63947479020631]
  In many real-world applications, the LiDAR points used by mass-produced robots and vehicles usually have fewer beams than that in large-scale public datasets. We propose the LiDAR Distillation to bridge the domain gap induced by different LiDAR beams for 3D object detection.
  arXiv  Detail & Related papers  (2022-03-28T17:59:02Z)
• Learning Moving-Object Tracking with FMCW LiDAR [53.05551269151209]
  We propose a learning-based moving-object tracking method utilizing our newly developed LiDAR sensor, Frequency Modulated Continuous Wave (FMCW) LiDAR. Given the labels, we propose a contrastive learning framework, which pulls together the features from the same instance in embedding space and pushes apart the features from different instances to improve the tracking quality.
  arXiv  Detail & Related papers  (2022-03-02T09:11:36Z)
• End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization [87.56144220508587]
  We take a new route to learn to optimize the LiDAR beam configuration for a given application. We propose a reinforcement learning-based learning-to-optimize framework to automatically optimize the beam configuration. Our method is especially useful when a low-resolution (low-cost) LiDAR is needed.
  arXiv  Detail & Related papers  (2022-01-11T09:46:31Z)
• Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR [22.202192422883122]
  We propose a novel two-stage network to advance the self-supervised monocular dense depth learning. Our model fuses monocular image features and sparse LiDAR features to predict initial depth maps. Our model outperforms the state-of-the-art sparse-LiDAR-based method (Pseudo-LiDAR++) by more than 68% for the downstream task monocular 3D object detection.
  arXiv  Detail & Related papers  (2021-09-20T15:28:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.