The KITTI Vision Benchmark Suite

We present Hybrid Voxel Network (HVNet), a novel
one-stage unified network for point cloud based 3D
object detection for autonomous driving. Recent
studies show that 2D voxelization with per voxel
PointNet style feature extractor leads to accurate
and efficient detector for large 3D scenes. Since
the size of the feature map determines the
computation and memory cost, the size of the voxel
becomes a parameter that is hard to balance. A
smaller voxel size gives a better performance,
especially for small objects, but a longer
inference time. A larger voxel can cover the same
area with a smaller feature map, but fails to
capture intricate features and accurate location
for smaller objects. We present a Hybrid Voxel
network that solves this problem by fusing voxel
feature encoder (VFE) of different scales at
point-wise level and project into multiple pseudo-
image feature maps. We further propose an
attentive voxel feature encoding that outperforms
plain VFE and a feature fusion pyramid network to
aggregate multi-scale information at feature map
level. Experiments on the KITTI benchmark show
that a single HVNet achieves the best mAP among
all existing methods with a real time inference
speed of 31Hz.

TBD

@inproceedings{ye2020hvnet,
title={HVNet: Hybrid Voxel Network for LiDAR Based
3D Object Detection},
author={Maosheng Ye and Shuangjie Xu and Tongyi
Cao},
booktitle={CVPR},
year={2020}
}