  Recent advances in 3D object detection are made by developing the refinement
stage for voxel-based Region Proposal Networks (RPN) to better strike the
balance between accuracy and efficiency. A popular approach among
state-of-the-art frameworks is to divide proposals, or Regions of Interest
(ROI), into grids and extract features for each grid location before
synthesizing them to form ROI features. While achieving impressive
performances, such an approach involves several hand-crafted components (e.g.
grid sampling, set abstraction) which requires expert knowledge to be tuned
correctly. This paper proposes a data-driven approach to ROI feature computing
named APRO3D-Net which consists of a voxel-based RPN and a refinement stage
made of Vector Attention. Unlike the original multi-head attention, Vector
Attention assigns different weights to different channels within a point
feature, thus being able to capture a more sophisticated relation between
pooled points and ROI. Our method achieves a competitive performance of 84.85
AP for class Car at moderate difficulty on the validation set of KITTI and
47.03 mAP (average over 10 classes) on NuScenes while having the least
parameters compared to closely related methods and attaining an inference speed
at 15 FPS on NVIDIA V100 GPU. The code is released at
https://github.com/quan-dao/APRO3D-Net.
