3D object detectors for point clouds often rely on a pooling-based PointNet
to encode sparse points into grid-like voxels or pillars. In this paper, we
identify that the common PointNet design introduces an information bottleneck
that limits 3D object detection accuracy and scalability. To address this
limitation, we propose PVTransformer: a transformer-based point-to-voxel
architecture for 3D detection. Our key idea is to replace the PointNet pooling
operation with an attention module, leading to a better point-to-voxel
aggregation function. Our design respects the permutation invariance of sparse
3D points while being more expressive than the pooling-based PointNet.
Experimental results show our PVTransformer achieves much better performance
compared to the latest 3D object detectors. On the widely used Waymo Open
Dataset, our PVTransformer achieves state-of-the-art 76.5 mAPH L2,
outperforming the prior art of SWFormer by +1.7 mAPH L2.

信息瓶颈限制了 3D 物体检测的准确性和可扩展性，因此我们提出了基于 Transformer 的 PVTransformer 架构，通过用注意模块替换 PointNet 池化操作来改善点到体的聚合函数，从而在广泛使用的 Waymo Open Dataset 上实现了卓越的表现。