  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.
