SOGDet: Semantic-Occupancy Guided Multi-View 3D Object Detection

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SOGDet: Semantic-Occupancy Guided Multi-View 3D Object Detection
Title:
SOGDet: Semantic-Occupancy Guided Multi-View 3D Object Detection
Journal Title:
Proceedings of the AAAI Conference on Artificial Intelligence
Keywords:
Publication Date:
25 March 2024
Citation:
Zhou, Q., Cao, J., Leng, H., Yin, Y., Kun, Y., & Zimmermann, R. (2024). SOGDet: Semantic-Occupancy Guided Multi-View 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 38(7), 7668–7676. https://doi.org/10.1609/aaai.v38i7.28600
Abstract:
In the field of autonomous driving, accurate and comprehensive perception of the 3D environment is crucial. Bird's Eye View (BEV) based methods have emerged as a promising solution for 3D object detection using multi-view images as input. However, existing 3D object detection methods often ignore the physical context in the environment, such as sidewalk and vegetation, resulting in sub-optimal performance. In this paper, we propose a novel approach called SOGDet (Semantic-Occupancy Guided Multi-view 3D Object Detection), that leverages a 3D semantic-occupancy branch to improve the accuracy of 3D object detection. In particular, the physical context modeled by semantic occupancy helps the detector to perceive the scenes in a more holistic view. Our SOGDet is flexible to use and can be seamlessly integrated with most existing BEV-based methods. To evaluate its effectiveness, we apply this approach to several state-of-the-art baselines and conduct extensive experiments on the exclusive nuScenes dataset. Our results show that SOGDet consistently enhance the performance of three baseline methods in terms of nuScenes Detection Score (NDS) and mean Average Precision (mAP). This indicates that the combination of 3D object detection and 3D semantic occupancy leads to a more comprehensive perception of the 3D environment, thereby aiding build more robust autonomous driving systems. The codes are available at: https://github.com/zhouqiu/SOGDet.
License type:
Publisher Copyright
Funding Info:
This work is supported by the Advanced Research and Technology Innovation Centre (ARTIC), the National University of Singapore.
Description:
This material may not be retransmitted or redistributed without permission in writing from The Association for the Advancement of Artificial Intelligence. Permission to use document is granted, provided that (1) the copyright notice appears in all copies and that both the copyright notice and this permission notice appear, (2) use of such documents is for personal use only, and will not be copied or posted on any network computer or broadcast in any media, and (3) no modifications of any documents are made.
ISSN:
2159-5399
2374-3468
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