Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images

TL;DR

This paper introduces FCOS-LiDAR, a simple and fast 3D object detection method using range view images from LiDAR data, achieving comparable results to complex BEV-based methods with fewer operations.

Contribution

The paper demonstrates that a 2D convolution-based range view detector can match state-of-the-art performance and introduces a novel multi-frame fusion mechanism for range view data.

Findings

Achieves comparable accuracy to BEV-based detectors

Significantly faster and simpler detection pipeline

First to effectively fuse multi-frame LiDAR data in range view

Abstract

We present a simple yet effective fully convolutional one-stage 3D object detector for LiDAR point clouds of autonomous driving scenes, termed FCOS-LiDAR. Unlike the dominant methods that use the bird-eye view (BEV), our proposed detector detects objects from the range view (RV, a.k.a. range image) of the LiDAR points. Due to the range view's compactness and compatibility with the LiDAR sensors' sampling process on self-driving cars, the range view-based object detector can be realized by solely exploiting the vanilla 2D convolutions, departing from the BEV-based methods which often involve complicated voxelization operations and sparse convolutions. For the first time, we show that an RV-based 3D detector with standard 2D convolutions alone can achieve comparable performance to state-of-the-art BEV-based detectors while being significantly faster and simpler. More importantly, almost all previous range view-based detectors only focus on single-frame point clouds, since it is challenging to fuse multi-frame point clouds into a single range view. In this work, we tackle this challenging issue with a novel range view projection mechanism, and for the first time demonstrate the benefits of fusing multi-frame point clouds for a range-view based detector. Extensive experiments on nuScenes show the superiority of our proposed method and we believe that our work can be strong evidence that an RV-based 3D detector can compare favourably with the current mainstream BEV-based detectors.