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Our method, called Stereo R-CNN, extends Faster R-CNN for stereo\ninputs to simultaneously detect and associate object in left and right images.\nWe add extra branches after stereo Region Proposal Network (RPN) to predict\nsparse keypoints, viewpoints, and object dimensions, which are combined with 2D\nleft-right boxes to calculate a coarse 3D object bounding box. We then recover\nthe accurate 3D bounding box by a region-based photometric alignment using left\nand right RoIs. Our method does not require depth input and 3D position\nsupervision, however, outperforms all existing fully supervised image-based\nmethods. Experiments on the challenging KITTI dataset show that our method\noutperforms the state-of-the-art stereo-based method by around 30% AP on both\n3D detection and 3D localization tasks. Code has been released at\nhttps://github.com/HKUST-Aerial-Robotics/Stereo-RCNN.","url_abs":"http://arxiv.org/abs/1902.09738v2","url_pdf":"http://arxiv.org/pdf/1902.09738v2.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":"stereo-r-cnn-based-3d-object-detection-for","repo_url":"https://github.com/HKUST-Aerial-Robotics/Stereo-RCNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"stereo-r-cnn-based-3d-object-detection-for","repo_url":"https://github.com/ModelBunker/Stereo-RCNN-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"stereo-r-cnn-based-3d-object-detection-for","repo_url":"https://github.com/NANSHANB/StereoR-CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"stereo-r-cnn-based-3d-object-detection-for","repo_url":"https://github.com/srinu6/Stereo-3D-Object-Detection-for-Autonomous-Driving","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"3d-object-detection","task_name":"3D Object Detection"},{"task_slug":"3d-object-detection-from-stereo-images","task_name":"3D Object Detection From Stereo Images"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"region-proposal","task_name":"Region Proposal"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"}],"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":"Stereo R-CNN","rank_in_archive_order":11,"of":12,"metrics":{"AP75":"30.23"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.09738","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09738"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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