ステレオ画像を用いた自動運転向け3D物体検出Stereo R-CNN
Stereo R-CNN based 3D Object Detection for Autonomous Driving
ステレオ画像の左右で物体を同時検出・対応付けし、キーポイントや視点・寸法予測と光度整合で高精度な3Dバウンディングボックスを推定する手法を提案した。
著者: Peiliang Li, Xiaozhi Chen, Shaojie Shen
分類: cs.CV, cs.RO
原文アブストラクト
We propose a 3D object detection method for autonomous driving by fully exploiting the sparse and dense, semantic and geometry information in stereo imagery. Our method, called Stereo R-CNN, extends Faster R-CNN for stereo inputs to simultaneously detect and associate object in left and right images. We add extra branches after stereo Region Proposal Network (RPN) to predict sparse keypoints, viewpoints, and object dimensions, which are combined with 2D left-right boxes to calculate a coarse 3D object bounding box. We then recover the accurate 3D bounding box by a region-based photometric alignment using left and right RoIs. Our method does not require depth input and 3D position supervision, however, outperforms all existing fully supervised image-based methods. Experiments on the challenging KITTI dataset show that our method outperforms the state-of-the-art stereo-based method by around 30% AP on both 3D detection and 3D localization tasks. Code has been released at https://github.com/HKUST-Aerial-Robotics/Stereo-RCNN.
