# DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation Source: [DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:25:36.384Z Authors: Runyu Ding, Jihan Yang, Li Jiang, Xiaojuan Qi Published: 2022-04-04T15:52:55.000Z ## 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 -\> ScanNet and 3D-FRONT -\> S3DIS. Code is available at https://github.com/CVMI-Lab/DODA. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()