Vision-based roadside 3D object detection has attracted rising attention in
autonomous driving domain, since it encompasses inherent advantages in reducing
blind spots and expanding perception range. While previous work mainly focuses
on accurately estimating depth or height for 2D-to-3D mapping, ignoring the
position approximation error in the voxel pooling process. Inspired by this
insight, we propose a novel voxel pooling strategy to reduce such error, dubbed
BEVSpread. Specifically, instead of bringing the image features contained in a
frustum point to a single BEV grid, BEVSpread considers each frustum point as a
source and spreads the image features to the surrounding BEV grids with
adaptive weights. To achieve superior propagation performance, a specific
weight function is designed to dynamically control the decay speed of the
weights according to distance and depth. Aided by customized CUDA parallel
acceleration, BEVSpread achieves comparable inference time as the original
voxel pooling. Extensive experiments on two large-scale roadside benchmarks
demonstrate that, as a plug-in, BEVSpread can significantly improve the
performance of existing frustum-based BEV methods by a large margin of (1.12,
5.26, 3.01) AP in vehicle, pedestrian and cyclist.

基于视觉的道路三维物体检测中，提出了一种新的体素池化策略 BEVSpread，通过自适应权重将图像特征传播到周围的 BEV 格点，使得现有基于截锥体和 BEV 的方法在车辆、行人和骑行者方面得到明显的性能提升。