{"type":"ScholarlyArticle","title":"Bridging LiDAR Gaps: A Multi-LiDARs Domain Adaptation Dataset for 3D Semantic Segmentation","authors":[{"name":"Shaoyang Chen","slug":"shaoyang-chen","given":"Shaoyang","family":"Chen","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , Xiamen University"},{"name":"Bochun Yang","slug":"bochun-yang","given":"Bochun","family":"Yang","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , Xiamen University"},{"name":"Yan Xia","slug":"yan-xia","given":"Yan","family":"Xia","affiliation":"Technical University of Munich; Munich Center for Machine Learning (MCML)"},{"name":"Ming Cheng","slug":"ming-cheng","given":"Ming","family":"Cheng","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , Xiamen University"},{"name":"Siqi Shen","slug":"siqi-shen","given":"Siqi","family":"Shen","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , Xiamen University"},{"name":"Cheng Wang","slug":"cheng-wang","given":"Cheng","family":"Wang","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart Cities , Xiamen University"}],"abstract":"We focus on the domain adaptation problem for 3D semantic segmentation, addressing the challenge of data variability in point clouds collected by different LiDARs. Existing benchmarks often mix different types of datasets, which blurs and complicates segmentation evaluations. Here, we introduce a Multi-LiDARs Domain Adaptation Segmentation (MLDAS) dataset, which contains point-wise semantic annotated point clouds captured simultaneously by a 128-beam LiDAR, a 64-beam LiDAR, a 32-beam LiDAR. We select 31,875 scans from 2 representative scenarios: campus and urban street. Furthermore, we evaluate the current 3D segmentation unsupervised domain adaptation methods on the proposed dataset and propose Hierarchical Segmentation Network with Spatial Consistency (HSSC) as a novel knowledge transfer method to mitigate the domain gap significantly using spatial-temporal consistency constraints. Extensive experiments show that HSSC greatly improves the state-of-the-art cross-domain semantic segmentation methods. Our project is available at https://sychen320.github.io/projects/MLDAS.","doi":"10.24963/ijcai.2024/72","url":"https://ijcai.org/proceedings/2024/72","pdf":"https://ijcai.org/proceedings/2024/0072.pdf","markdown":"https://ijcai.org/proceedings/2024/72.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/72","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":72,"pages":{"first":650,"last":658},"keywords":["Computer Vision: CV: 3D computer vision"],"videos":[],"references":[{"position":1,"raw":"[Behley et al., 2019] Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, and Jurgen Gall. 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