论文标题
自动驾驶的转换 - 等价3D对象检测
Transformation-Equivariant 3D Object Detection for Autonomous Driving
论文作者
论文摘要
3D对象检测最近在自主驾驶中受到了越来越多的关注。 3D场景中的对象以各种取向分布。普通检测器不会明确对旋转和反射转换的变化进行建模。因此，鲁棒检测需要大型网络和广泛的数据增强。最近的Epoiriant网络通过在多个变换点云上应用共享网络来明确对转换变化进行建模，从而在对象几何形状建模中显示出巨大的潜力。但是，由于其较大的计算成本和缓慢的推理速度，很难将此类网络应用于3D对象检测。在这项工作中，我们提出了TED，这是一种有效的转换等值的3D检测器，以克服计算成本和速度问题。 TED首先应用稀疏的卷积主链来提取多通道变换 - 等级体素特征。然后将这些模棱两可的特征对齐并汇总为轻质和紧凑的表示，以进行高性能3D对象检测。在竞争激烈的KITTI 3D CAR检测排行榜上，TED以竞争性效率排名第一。
3D object detection received increasing attention in autonomous driving recently. Objects in 3D scenes are distributed with diverse orientations. Ordinary detectors do not explicitly model the variations of rotation and reflection transformations. Consequently, large networks and extensive data augmentation are required for robust detection. Recent equivariant networks explicitly model the transformation variations by applying shared networks on multiple transformed point clouds, showing great potential in object geometry modeling. However, it is difficult to apply such networks to 3D object detection in autonomous driving due to its large computation cost and slow reasoning speed. In this work, we present TED, an efficient Transformation-Equivariant 3D Detector to overcome the computation cost and speed issues. TED first applies a sparse convolution backbone to extract multi-channel transformation-equivariant voxel features; and then aligns and aggregates these equivariant features into lightweight and compact representations for high-performance 3D object detection. On the highly competitive KITTI 3D car detection leaderboard, TED ranked 1st among all submissions with competitive efficiency.
