Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds

TL;DR

This paper introduces BRNet, a novel 3D object detection method in point clouds that back-traces representative points to improve localization accuracy, outperforming existing methods on major datasets.

Contribution

The paper proposes a back-tracing strategy for representative points in 3D detection, enhancing the utilization of point cloud information and improving detection performance.

Findings

Achieves +7.5% mAP on ScanNet V2

Achieves +4.7% mAP on SUN RGB-D

Outperforms state-of-the-art methods significantly

Abstract

3D object detection in point clouds is a challenging vision task that benefits various applications for understanding the 3D visual world. Lots of recent research focuses on how to exploit end-to-end trainable Hough voting for generating object proposals. However, the current voting strategy can only receive partial votes from the surfaces of potential objects together with severe outlier votes from the cluttered backgrounds, which hampers full utilization of the information from the input point clouds. Inspired by the back-tracing strategy in the conventional Hough voting methods, in this work, we introduce a new 3D object detection method, named as Back-tracing Representative Points Network (BRNet), which generatively back-traces the representative points from the vote centers and also revisits complementary seed points around these generated points, so as to better capture the fine local structural features surrounding the potential objects from the raw point clouds. Therefore, this bottom-up and then top-down strategy in our BRNet enforces mutual consistency between the predicted vote centers and the raw surface points and thus achieves more reliable and flexible object localization and class prediction results. Our BRNet is simple but effective, which significantly outperforms the state-of-the-art methods on two large-scale point cloud datasets, ScanNet V2 (+7.5% in terms of mAP@0.50) and SUN RGB-D (+4.7% in terms of mAP@0.50), while it is still lightweight and efficient. Code will be available at https://github.com/cheng052/BRNet.