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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 1: AAAI-22 Technical Tracks 1

SASA: Semantics-Augmented Set Abstraction for Point-Based 3D Object Detection

February 1, 2023

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Authors

Chen Chen

The University of Sydney, Australia


Zhe Chen

The University of Sydney, Australia


Jing Zhang

The University of Sydney, Australia


Dacheng Tao

JD Explore Academy, China


DOI:

10.1609/aaai.v36i1.19897


Abstract:

Although point-based networks are demonstrated to be accurate for 3D point cloud modeling, they are still falling behind their voxel-based competitors in 3D detection. We observe that the prevailing set abstraction design for down-sampling points may maintain too much unimportant background information that can affect feature learning for detecting objects. To tackle this issue, we propose a novel set abstraction method named Semantics-Augmented Set Abstraction (SASA). Technically, we first add a binary segmentation module as the side output to help identify foreground points. Based on the estimated point-wise foreground scores, we then propose a semantics-guided point sampling algorithm to help retain more important foreground points during down-sampling. In practice, SASA shows to be effective in identifying valuable points related to foreground objects and improving feature learning for point-based 3D detection. Additionally, it is an easy-to-plug-in module and able to boost various point-based detectors, including single-stage and two-stage ones. Extensive experiments on the popular KITTI and nuScenes datasets validate the superiority of SASA, lifting point-based detection models to reach comparable performance to state-of-the-art voxel-based methods. Code is available at https://github.com/blakechen97/SASA.

Topics: AAAI

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HOW TO CITE:

Chen Chen||Zhe Chen||Jing Zhang||Dacheng Tao SASA: Semantics-Augmented Set Abstraction for Point-Based 3D Object Detection Proceedings of the AAAI Conference on Artificial Intelligence (2022) 221-229.

Chen Chen||Zhe Chen||Jing Zhang||Dacheng Tao SASA: Semantics-Augmented Set Abstraction for Point-Based 3D Object Detection AAAI 2022, 221-229.

Chen Chen||Zhe Chen||Jing Zhang||Dacheng Tao (2022). SASA: Semantics-Augmented Set Abstraction for Point-Based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 221-229.

Chen Chen||Zhe Chen||Jing Zhang||Dacheng Tao. SASA: Semantics-Augmented Set Abstraction for Point-Based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.221-229.

Chen Chen||Zhe Chen||Jing Zhang||Dacheng Tao. 2022. SASA: Semantics-Augmented Set Abstraction for Point-Based 3D Object Detection. "Proceedings of the AAAI Conference on Artificial Intelligence". 221-229.

Chen Chen||Zhe Chen||Jing Zhang||Dacheng Tao. (2022) "SASA: Semantics-Augmented Set Abstraction for Point-Based 3D Object Detection", Proceedings of the AAAI Conference on Artificial Intelligence, p.221-229

Chen Chen||Zhe Chen||Jing Zhang||Dacheng Tao, "SASA: Semantics-Augmented Set Abstraction for Point-Based 3D Object Detection", AAAI, p.221-229, 2022.

Chen Chen||Zhe Chen||Jing Zhang||Dacheng Tao. "SASA: Semantics-Augmented Set Abstraction for Point-Based 3D Object Detection". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.221-229.

Chen Chen||Zhe Chen||Jing Zhang||Dacheng Tao. "SASA: Semantics-Augmented Set Abstraction for Point-Based 3D Object Detection". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 221-229.

Chen Chen||Zhe Chen||Jing Zhang||Dacheng Tao. SASA: Semantics-Augmented Set Abstraction for Point-Based 3D Object Detection. AAAI[Internet]. 2022[cited 2023]; 221-229.


ISSN: 2374-3468


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