Towards Long-Range 3D Object Detection for Autonomous Vehicles

TL;DR

This paper enhances long-range 3D object detection for autonomous vehicles by combining specialized detection networks and augmenting LiDAR data with virtual points, significantly improving detection performance at extended distances.

Contribution

It introduces a dual-range detection framework and utilizes multimodal virtual points to address sparsity and label imbalance in long-range LiDAR detection.

Findings

MVP augmentation improves long-range detection accuracy.

Range experts are computationally efficient and effective.

Combining methods yields better long-range detection performance.

Abstract

3D object detection at long range is crucial for ensuring the safety and efficiency of self driving vehicles, allowing them to accurately perceive and react to objects, obstacles, and potential hazards from a distance. But most current state of the art LiDAR based methods are range limited due to sparsity at long range, which generates a form of domain gap between points closer to and farther away from the ego vehicle. Another related problem is the label imbalance for faraway objects, which inhibits the performance of Deep Neural Networks at long range. To address the above limitations, we investigate two ways to improve long range performance of current LiDAR based 3D detectors. First, we combine two 3D detection networks, referred to as range experts, one specializing at near to mid range objects, and one at long range 3D detection. To train a detector at long range under a scarce label regime, we further weigh the loss according to the labelled point's distance from ego vehicle. Second, we augment LiDAR scans with virtual points generated using Multimodal Virtual Points (MVP), a readily available image-based depth completion algorithm. Our experiments on the long range Argoverse2 (AV2) dataset indicate that MVP is more effective in improving long range performance, while maintaining a straightforward implementation. On the other hand, the range experts offer a computationally efficient and simpler alternative, avoiding dependency on image-based segmentation networks and perfect camera-LiDAR calibration.