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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 4: AAAI-21 Technical Tracks 4

ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation

February 1, 2023

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Authors

Sicheng Zhao

University of California, Berkeley


Yezhen Wang

Didi Chuxing University of California, San Diego


Bo Li

University of California, Berkeley


Bichen Wu

Facebook Inc.


Yang Gao

Tsinghua University


Pengfei Xu

Didi Chuxing


Trevor Darrell

University of California, Berkeley


Kurt Keutzer

University of California, Berkeley


DOI:

10.1609/aaai.v35i4.16464


Abstract:

Due to its robust and precise distance measurements, LiDAR plays an important role in scene understanding for autonomous driving. Training deep neural networks (DNNs) on LiDAR data requires large-scale point-wise annotations, which are time-consuming and expensive to obtain. Instead, simulation-to-real domain adaptation (SRDA) trains a DNN using unlimited synthetic data with automatically generated labels and transfers the learned model to real scenarios. Existing SRDA methods for LiDAR point cloud segmentation mainly employ a multi-stage pipeline and focus on feature-level alignment. They require prior knowledge of real-world statistics and ignore the pixel-level dropout noise gap and the spatial feature gap between different domains. In this paper, we propose a novel end-to-end framework, named ePointDA, to address the above issues. Specifically, ePointDA consists of three modules: self-supervised dropout noise rendering, statistics-invariant and spatially-adaptive feature alignment, and transferable segmentation learning. The joint optimization enables ePointDA to bridge the domain shift at the pixel-level by explicitly rendering dropout noise for synthetic LiDAR and at the feature-level by spatially aligning the features between different domains, without requiring the real-world statistics. Extensive experiments adapting from synthetic GTA-LiDAR to real KITTI and SemanticKITTI demonstrate the superiority of ePointDA for LiDAR point cloud segmentation.

Topics: AAAI

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HOW TO CITE:

Sicheng Zhao||Yezhen Wang||Bo Li||Bichen Wu||Yang Gao||Pengfei Xu||Trevor Darrell||Kurt Keutzer ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation Proceedings of the AAAI Conference on Artificial Intelligence (2021) 3500-3509.

Sicheng Zhao||Yezhen Wang||Bo Li||Bichen Wu||Yang Gao||Pengfei Xu||Trevor Darrell||Kurt Keutzer ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation AAAI 2021, 3500-3509.

Sicheng Zhao||Yezhen Wang||Bo Li||Bichen Wu||Yang Gao||Pengfei Xu||Trevor Darrell||Kurt Keutzer (2021). ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 3500-3509.

Sicheng Zhao||Yezhen Wang||Bo Li||Bichen Wu||Yang Gao||Pengfei Xu||Trevor Darrell||Kurt Keutzer. ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.3500-3509.

Sicheng Zhao||Yezhen Wang||Bo Li||Bichen Wu||Yang Gao||Pengfei Xu||Trevor Darrell||Kurt Keutzer. 2021. ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation. "Proceedings of the AAAI Conference on Artificial Intelligence". 3500-3509.

Sicheng Zhao||Yezhen Wang||Bo Li||Bichen Wu||Yang Gao||Pengfei Xu||Trevor Darrell||Kurt Keutzer. (2021) "ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation", Proceedings of the AAAI Conference on Artificial Intelligence, p.3500-3509

Sicheng Zhao||Yezhen Wang||Bo Li||Bichen Wu||Yang Gao||Pengfei Xu||Trevor Darrell||Kurt Keutzer, "ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation", AAAI, p.3500-3509, 2021.

Sicheng Zhao||Yezhen Wang||Bo Li||Bichen Wu||Yang Gao||Pengfei Xu||Trevor Darrell||Kurt Keutzer. "ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.3500-3509.

Sicheng Zhao||Yezhen Wang||Bo Li||Bichen Wu||Yang Gao||Pengfei Xu||Trevor Darrell||Kurt Keutzer. "ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 3500-3509.

Sicheng Zhao||Yezhen Wang||Bo Li||Bichen Wu||Yang Gao||Pengfei Xu||Trevor Darrell||Kurt Keutzer. ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation. AAAI[Internet]. 2021[cited 2023]; 3500-3509.


ISSN: 2374-3468


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