3D Object Detection for Autonomous Driving: A Survey

TL;DR

This survey comprehensively reviews 3D object detection methods for autonomous driving, covering sensors, datasets, techniques, and performance, while analyzing their strengths, weaknesses, and future research directions.

Contribution

It fills a gap by systematically structuring and analyzing the growing body of 3D detection research for autonomous driving, including quantitative comparisons and case studies.

Findings

State-of-the-art methods vary in accuracy and robustness.

Recent techniques improve detection in occluded and unstructured environments.

Future directions include better sensor fusion and real-time performance.

Abstract

Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of perception stack especially for the sake of path planning, motion prediction, and collision avoidance etc. Taking a quick glance at the progress we have made, we attribute challenges to visual appearance recovery in the absence of depth information from images, representation learning from partially occluded unstructured point clouds, and semantic alignments over heterogeneous features from cross modalities. Despite existing efforts, 3D object detection for autonomous driving is still in its infancy. Recently, a large body of literature have been investigated to address this 3D vision task. Nevertheless, few investigations have looked into collecting and structuring this growing knowledge. We therefore aim to fill this gap in a comprehensive survey, encompassing all the main concerns including sensors, datasets, performance metrics and the recent state-of-the-art detection methods, together with their pros and cons. Furthermore, we provide quantitative comparisons with the state of the art. A case study on fifteen selected representative methods is presented, involved with runtime analysis, error analysis, and robustness analysis. Finally, we provide concluding remarks after an in-depth analysis of the surveyed works and identify promising directions for future work.