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Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation [article]

Aoran Xiao, Jiaxing Huang, Dayan Guan, Fangneng Zhan, Shijian Lu
2021 arXiv   pre-print
We address this issue by collecting SynLiDAR, a large-scale synthetic LiDAR dataset that contains point-wise annotated point clouds with accurate geometric shapes and comprehensive semantic classes.  ...  Knowledge transfer from synthetic to real data has been widely studied to mitigate data annotation constraints in various computer vision tasks such as semantic segmentation.  ...  It is the largest realworld sequential LiDAR point cloud dataset for semantic segmentation to the best of our knowledge.  ... 
arXiv:2107.05399v2 fatcat:flsxvlk2pfblboid4mea4qkeq4

STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset [article]

Meida Chen, Qingyong Hu, Thomas Hugues, Andrew Feng, Yu Hou, Kyle McCullough, Lucio Soibelman
2022 arXiv   pre-print
Based on the proposed pipeline, we present a richly-annotated synthetic 3D aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16 km^2 of landscapes and up to 18 fine-grained semantic  ...  For verification purposes, we also provide a parallel dataset collected from four areas in the real environment.  ...  cloud dataset for automatic segmentation and classification.IJRR (2018) 2, 4 98.Xiao, A., Huang, J., Guan, D., Zhan, F., Lu, S.: Synlidar: Learning from synthetic lidar sequential point cloud for semantic  ... 
arXiv:2203.09065v1 fatcat:jjosk5xwsfhx7jguehkbveqip4