论文标题
立体声：3D对象检测的暹罗管道
Stereo Frustums: A Siamese Pipeline for 3D Object Detection
论文作者
论文摘要
本文提出了一个轻加权的立体声粉丝匹配3D异议检测的模块。所提出的框架利用了高性能的2D检测器和点云分段网络来回归3D边界框，用于自动驾驶车辆。该模块没有执行传统的立体声匹配来计算差异，而是直接从左视图和右视图中以2D提案为输入。基于从精心校准的立体声摄像机中恢复的外两极约束，我们提出了四种匹配算法，以搜索立体声图像对之间的每个建议的最佳匹配。每个匹配对提出了场景的分割，然后将其馈入3D边界框回归网络。 Kitti数据集的广泛实验的结果表明，所提出的暹罗管道的表现优于最新立体声的3D边界框回归方法。
The paper proposes a light-weighted stereo frustums matching module for 3D objection detection. The proposed framework takes advantage of a high-performance 2D detector and a point cloud segmentation network to regress 3D bounding boxes for autonomous driving vehicles. Instead of performing traditional stereo matching to compute disparities, the module directly takes the 2D proposals from both the left and the right views as input. Based on the epipolar constraints recovered from the well-calibrated stereo cameras, we propose four matching algorithms to search for the best match for each proposal between the stereo image pairs. Each matching pair proposes a segmentation of the scene which is then fed into a 3D bounding box regression network. Results of extensive experiments on KITTI dataset demonstrate that the proposed Siamese pipeline outperforms the state-of-the-art stereo-based 3D bounding box regression methods.
