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However, its performance is still inferior compared with LiDAR-based detection algorithms. To detect and localize accurate 3D bounding boxes, LiDAR-based models can encode accurate object boundaries and surface normal directions from LiDAR point clouds. However, the detection results of stereo-based detectors are easily affected by the erroneous depth features due to the limitation of stereo matching. To solve the problem, we propose LIGA-Stereo (LiDAR Geometry Aware Stereo Detector) to learn stereo-based 3D detectors under the guidance of high-level geometry-aware representations of LiDAR-based detection models. In addition, we found existing voxel-based stereo detectors failed to learn semantic features effectively from indirect 3D supervisions. We attach an auxiliary 2D detection head to provide direct 2D semantic supervisions. Experiment results show that the above two strategies improved the geometric and semantic representation capabilities. Compared with the state-of-the-art stereo detector, our method has improved the 3D detection performance of cars, pedestrians, cyclists by 10.44%, 5.69%, 5.97% mAP respectively on the official KITTI benchmark. The gap between stereo-based and LiDAR-based 3D detectors is further narrowed.","url_abs":"https://arxiv.org/abs/2108.08258v1","url_pdf":"https://arxiv.org/pdf/2108.08258v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"liga-stereo-learning-lidar-geometry-aware","repo_url":"https://github.com/xy-guo/LIGA-Stereo","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"3d-object-detection-from-stereo-images","task_name":"3D Object Detection From Stereo Images"},{"task_slug":"3d-geometry","task_name":"3D geometry"},{"task_slug":"stereo-matching-1","task_name":"Stereo Matching"}],"methods":[{"method_slug":"aware","method_name":"AWARE"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-object-detection-from-stereo-images-on-1","task":"3D Object Detection From Stereo Images","dataset":"KITTI Cars Moderate","model":"LIGA-Stereo","rank_in_archive_order":2,"of":12,"metrics":{"AP75":"64.66"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-detection-from-stereo-images-on-3","task":"3D Object Detection From Stereo Images","dataset":"KITTI Cyclists Moderate","model":"LIGA-Stereo","rank_in_archive_order":2,"of":5,"metrics":{"AP50":"36.86"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-detection-from-stereo-images-on-2","task":"3D Object Detection From Stereo Images","dataset":"KITTI Pedestrians Moderate","model":"LIGA-Stereo","rank_in_archive_order":2,"of":6,"metrics":{"AP50":"30.00"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2108.08258","atlas_url":"https://app.syntology.ai/?focus=2108.08258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.08258"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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