Haiyang Wang, Shaoshuai Shi, Ze Yang, Rongyao Fang, Qi Qian...
TL;DR本文提出了 RBGNet 框架,一种基于投票的 3D 检测器,用于从点云中精确检测 3D 物体。该框架使用基于射线的特征分组模块来聚合物体表面的点特征,以了解物体的形状以增强聚类特征,从而预测 3D 边界框。此外,作者还提出了一种新颖的前景偏置采样策略,以在下采样过程中采样更多物体表面上的点,并显着提高检测性能。
Abstract
As a fundamental problem in computer vision, 3d object detection is experiencing rapid growth. To extract the point-wise features from the irregularly and sparsely distributed points, previous methods usually take a fea