Copy-Rotate-Paste Augmentation for Point Cloud Segmentation
Chen, Bike; Gong, Chen; Tikanmäki, Antti; Röning, Juha (2025-05-19)
IEEE
19.05.2025
B. Chen, C. Gong, A. Tikanmäki and J. Röning, "Copy-Rotate-Paste Augmentation for Point Cloud Segmentation," in IEEE Signal Processing Letters, vol. 32, pp. 2209-2213, 2025, doi: 10.1109/LSP.2025.3571409
https://creativecommons.org/licenses/by/4.0/
© 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
https://creativecommons.org/licenses/by/4.0/
© 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202506034102
https://urn.fi/URN:NBN:fi:oulu-202506034102
Tiivistelmä
Abstract
Point cloud segmentation (PCS) aims to classify each point in a point cloud. The task plays an important role in robotics and remote sensing. However, existing copy-paste and copy-rotate-paste augmentation techniques cause undesirable artifacts in the augmented point cloud and cannot effectively copy and paste interesting objects from the whole training dataset, bringing difficulty in training image-points fused models and leading to sub-optimal PCS performance. In this paper, we propose an improved virtual range image-guided copy-rotate-paste (VRCrop++) strategy and a global copy-rotate-paste (GCrop) technique. VRCrop++ and GCrop remove unwanted artifacts in the augmented point cloud by a simple but effective “point-to-patch” strategy in the pasting step. Besides, GCrop copies the objects globally with the reciprocal of the point distribution ratio and effectively pastes the objects considering the minimal overlapping region. Extensive experiments conducted on SemanticKITTI and SemanticPOSS datasets demonstrate that with VRCrop++ and GCrop, the existing range image-points fused models consistently surpass their counterparts.
Point cloud segmentation (PCS) aims to classify each point in a point cloud. The task plays an important role in robotics and remote sensing. However, existing copy-paste and copy-rotate-paste augmentation techniques cause undesirable artifacts in the augmented point cloud and cannot effectively copy and paste interesting objects from the whole training dataset, bringing difficulty in training image-points fused models and leading to sub-optimal PCS performance. In this paper, we propose an improved virtual range image-guided copy-rotate-paste (VRCrop++) strategy and a global copy-rotate-paste (GCrop) technique. VRCrop++ and GCrop remove unwanted artifacts in the augmented point cloud by a simple but effective “point-to-patch” strategy in the pasting step. Besides, GCrop copies the objects globally with the reciprocal of the point distribution ratio and effectively pastes the objects considering the minimal overlapping region. Extensive experiments conducted on SemanticKITTI and SemanticPOSS datasets demonstrate that with VRCrop++ and GCrop, the existing range image-points fused models consistently surpass their counterparts.
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