3D Object Detection for Autonomous Driving: A Survey

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.

The manuscript is accepted by Pattern Recognition on 14 May 2022

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• A Multimodal Hybrid Late-Cascade Fusion Network for Enhanced 3D Object Detection
• Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities
• Composite Convolution: a Flexible Operator for Deep Learning on 3D Point Clouds
• All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehicles
• InScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios
• LCF3D: A Robust and Real-Time Late-Cascade Fusion Framework for 3D Object Detection in Autonomous Driving
• Benchmarking the Spatial Robustness of DNNs via Natural and Adversarial Localized Corruptions
• DPO: Dual-Perturbation Optimization for Test-time Adaptation in 3D Object Detection
• Network Optimization Aspects of Autonomous Vehicles: Challenges and Future Directions