Articles | Volume 17, issue 1
https://doi.org/10.5194/ms-17-221-2026
https://doi.org/10.5194/ms-17-221-2026
Research article
 | 
11 Mar 2026
Research article |  | 11 Mar 2026

GAPS: Group-wise Affine-Perturbed Serialization for efficient 3D semantic segmentation

Yubin Tang, Yangchen Liu, and Zichuan Fan

Cited articles

Armeni, I., Sener, O., Zamir, A. R., Jiang, H., Brilakis, I., Fischer, M., and Savarese, S.: 3D Semantic Parsing of Large-Scale Indoor Spaces, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016, IEEE, 1534–1543, https://doi.org/10.1109/CVPR.2016.170, 2016a. a
Armeni, I., Sener, O., Zamir, A. R., Jiang, H., Brilakis, I., Fischer, M., and Savarese, S.: Stanford Large-Scale 3D Indoor Spaces Dataset (S3DIS), Redivis [data set], https://doi.org/10.57761/gk3g-wc33, 2016b. a
Charles, R. Q., Su, H., Kaichun, M., and Guibas, L. J.: PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017, IEEE, 77–85, https://doi.org/10.1109/CVPR.2017.16, 2017. a, b
Chen, J., Yu, L., and Wang, W.: Hilbert Space Filling Curve Based Scan-Order for Point Cloud Attribute Compression, IEEE T. Image Process., 31, 4609–4621, https://doi.org/10.1109/TIP.2022.3186532, 2022. a
Chen, M., Guo, H., Qian, R., Gong, G., and Cheng, H.: Visual simultaneous localization and mapping (vSLAM) algorithm based on improved Vision Transformer semantic segmentation in dynamic scenes, Mech. Sci., 15, 1–16, https://doi.org/10.5194/ms-15-1-2024, 2024. a
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Short summary
Current methods for analyzing three-dimensional points often fail to capture complex shapes because they scan in fixed directions. We introduce Group-wise Affine-Perturbed Serialization to solve this. Our method scans data along diverse, angled paths simultaneously to better capture geometry. This approach outperforms existing technologies in terms of accuracy without adding computational delay. It enables computers to understand real-world environments more precisely and efficiently. 
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