论文标题
可重构体素：基于激光雷达的点云的新表示
Reconfigurable Voxels: A New Representation for LiDAR-Based Point Clouds
论文作者
论文摘要
LIDAR是自主驾驶系统感知环境的重要方法。 LIDAR获得的点云通常表现出稀疏和不规则的分布，因此对检测3D对象（尤其是小而遥远的物体）面临巨大挑战。为了解决这个困难，我们提出了可重构体素，这是一种从3D点云中构造表示形式的新方法。具体而言，我们设计了一个有偏见的随机步行方案，该方案基于局部空间分布自适应地覆盖了每个社区，并通过在所选邻居中集成点来产生表示形式。我们从经验上发现，这种方法有效地提高了体素特征的稳定性，尤其是对于稀疏地区。在包括Nuscenes，Lyft和Kitti在内的多个基准测试的实验结果表明，这种新表示形式可以显着改善小型和遥远对象的检测性能，而不会产生明显的开销成本。
LiDAR is an important method for autonomous driving systems to sense the environment. The point clouds obtained by LiDAR typically exhibit sparse and irregular distribution, thus posing great challenges to the detection of 3D objects, especially those that are small and distant. To tackle this difficulty, we propose Reconfigurable Voxels, a new approach to constructing representations from 3D point clouds. Specifically, we devise a biased random walk scheme, which adaptively covers each neighborhood with a fixed number of voxels based on the local spatial distribution and produces a representation by integrating the points in the chosen neighbors. We found empirically that this approach effectively improves the stability of voxel features, especially for sparse regions. Experimental results on multiple benchmarks, including nuScenes, Lyft, and KITTI, show that this new representation can remarkably improve the detection performance for small and distant objects, without incurring noticeable overhead costs.
