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

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

Keywords:

Vision for Robotics & Autonomous Driving, Transfer/Adaptation/Multi-task/Meta/Automated Learning

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.

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Published

2021-05-18

How to Cite

Zhao, S., Wang, Y., Li, B., Wu, B., Gao, Y., Xu, P., Darrell, T., & Keutzer, K. (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, 35(4), 3500-3509. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16464

Issue

Section

AAAI Technical Track on Computer Vision III