3D Object Detection for Autonomous Driving: A Survey
- URL: http://arxiv.org/abs/2106.10823v1
- Date: Mon, 21 Jun 2021 03:17:20 GMT
- Title: 3D Object Detection for Autonomous Driving: A Survey
- Authors: Rui Qian, Xin Lai, Xirong Li
- Abstract summary: 3D object detection serves as the core basis of such perception system.
Despite existing efforts, 3D object detection on point clouds is still in its infancy.
Recent state-of-the-art detection methods with their pros and cons are presented.
- Score: 14.772968858398043
- License: http://creativecommons.org/licenses/by/4.0/
- 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 such perception system especially for the sake of
path planning, motion prediction, collision avoidance, etc. Generally, stereo
or monocular images with corresponding 3D point clouds are already standard
layout for 3D object detection, out of which point clouds are increasingly
prevalent with accurate depth information being provided. Despite existing
efforts, 3D object detection on point clouds is still in its infancy due to
high sparseness and irregularity of point clouds by nature, misalignment view
between camera view and LiDAR bird's eye of view for modality synergies,
occlusions and scale variations at long distances, etc. Recently, profound
progress has been made in 3D object detection, with a large body of literature
being investigated to address this vision task. As such, we present a
comprehensive review of the latest progress in this field covering all the main
topics including sensors, fundamentals, and the recent state-of-the-art
detection methods with their pros and cons. Furthermore, we introduce metrics
and provide quantitative comparisons on popular public datasets. The avenues
for future work are going to be judiciously identified after an in-deep
analysis of the surveyed works. Finally, we conclude this paper.
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