DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Indoor Semantic Segmentation

• URL: http://arxiv.org/abs/2204.01599v1
• Date: Mon, 4 Apr 2022 15:52:55 GMT
• Title: DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Indoor Semantic Segmentation
• Authors: Runyu Ding, Jihan Yang, Li Jiang, Xiaojuan Qi
• Abstract summary: We propose a Data-Oriented Domain Adaptation (DODA) framework to mitigate pattern and context gaps. Our DODA encompasses virtual scan simulation to imitate real-world point cloud patterns and tail-aware cuboid mixing to alleviate the interior context gap. Our DODA surpasses existing UDA approaches by over 13% on both 3D-FRONT $rightarrow$ ScanNet and 3D-FRONT $rightarrow$ S3DIS.
• Score: 36.37396175140793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning approaches achieve prominent success in 3D semantic segmentation. However, collecting densely annotated real-world 3D datasets is extremely time-consuming and expensive. Training models on synthetic data and generalizing on real-world scenarios becomes an appealing alternative, but unfortunately suffers from notorious domain shifts. In this work, we propose a Data-Oriented Domain Adaptation (DODA) framework to mitigate pattern and context gaps caused by different sensing mechanisms and layout placements across domains. Our DODA encompasses virtual scan simulation to imitate real-world point cloud patterns and tail-aware cuboid mixing to alleviate the interior context gap with a cuboid-based intermediate domain. The first unsupervised sim-to-real adaptation benchmark on 3D indoor semantic segmentation is also built on 3D-FRONT, ScanNet and S3DIS along with 7 popular Unsupervised Domain Adaptation (UDA) methods. Our DODA surpasses existing UDA approaches by over 13% on both 3D-FRONT $\rightarrow$ ScanNet and 3D-FRONT $\rightarrow$ S3DIS. Code will be available.

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