3D Object Detection for Autonomous Driving: A Survey. (October 2022)

• Record Type: Journal Article
• Authors: Qian, Rui; Lai, Xin; Li, Xirong
• Is Part Of: Pattern recognition. Volume 130(2022)
• Publication Date: 2022-10
• Subjects: 3D object detection -- Autonomous driving -- Point clouds; 02–07; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108796
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Notice that no recent literature exists to collect the growing knowledge concerning 3D object detection, we fill this gap by starting with several basic concepts, providing a glimpse of evolution of 3D object detection, together with comprehensive comparisons on publicly available datasets being manifested, with pros and cons being judiciously presented. Witnessing the absence of a universal consensus on taxonomy with respect to 3D object detection, we contribute to the maturity of the taxonomy, which keeps a good continuity of existing efforts as well as adapts new branches for dynamics. We present a case study on fifteen selected models among surveyed works, with regard to runtime analysis, error analysis, and robustness analysis closely. We argue that what mainly restricts the performance of detection is 3D location error based on our findings. 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.