Attention-based Proposals Refinement for 3D Object Detection

Recent advances in 3D object detection is 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 feature for each grid location before synthesizing them to form ROI feature. While achieving impressive performances, such an approach involves a number of 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. Experiments on KITTI \textit{validation} set show that our method achieves competitive performance of 84.84 AP for class Car at Moderate difficulty while having the least parameters compared to closely related methods and attaining a quasi-real time inference speed at 15 FPS on NVIDIA V100 GPU. The code is released in https://github.com/quan-dao/APRO3D-Net.