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Impact of color and mixing proportion of synthetic point clouds on semantic segmentation.

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Title: Impact of color and mixing proportion of synthetic point clouds on semantic segmentation.
Authors: Zhou, Shaojie1,2 (AUTHOR), Lin, Jia-Rui1,2 (AUTHOR) lin611@tsinghua.edu.cn, Pan, Peng1,2 (AUTHOR), Pan, Yuandong3 (AUTHOR), Brilakis, Ioannis3 (AUTHOR)
Source: Automation in Construction. Mar2025, Vol. 171, pN.PAG-N.PAG. 1p.
Subjects: Point cloud, Deep learning, Built environment, Color, Scarcity
Abstract: Deep learning (DL)-based point cloud segmentation is essential for understanding built environment. Despite synthetic point clouds (SPC) having the potential to compensate for data shortage, how synthetic color and mixing proportion impact DL-based segmentation remains a long-standing question. Therefore, this paper addresses this question with extensive experiments by introducing: 1) method to generate SPC with real colors and uniform colors from BIM, and 2) enhanced benchmarks for better performance evaluation. Experiments on DL models including PointNet, PointNet++, and DGCNN show that model performance on SPC with real colors outperforms that on SPC with uniform colors by 8.2 % + on both OA and mIoU. Furthermore, a higher than 70 % mixing proportion of SPC usually leads to better performance. And SPC can replace real ones to train a DL model for detecting large and flat building elements. Overall, this paper unveils the performance-improving mechanism of SPC and brings new insights to boost SPC's value. • A framework to generate synthetic point clouds based on BIM. • Enhanced benchmarks for better performance evaluation • A synthetic dataset with real color outperforms that with uniform color by 8.2 % +. • A higher than 70 % mixing proportion of synthetic data usually leads to better performance. • Synthetic point clouds can replace real ones for detecting large and flat elements. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Deep learning (DL)-based point cloud segmentation is essential for understanding built environment. Despite synthetic point clouds (SPC) having the potential to compensate for data shortage, how synthetic color and mixing proportion impact DL-based segmentation remains a long-standing question. Therefore, this paper addresses this question with extensive experiments by introducing: 1) method to generate SPC with real colors and uniform colors from BIM, and 2) enhanced benchmarks for better performance evaluation. Experiments on DL models including PointNet, PointNet++, and DGCNN show that model performance on SPC with real colors outperforms that on SPC with uniform colors by 8.2 % + on both OA and mIoU. Furthermore, a higher than 70 % mixing proportion of SPC usually leads to better performance. And SPC can replace real ones to train a DL model for detecting large and flat building elements. Overall, this paper unveils the performance-improving mechanism of SPC and brings new insights to boost SPC's value. • A framework to generate synthetic point clouds based on BIM. • Enhanced benchmarks for better performance evaluation • A synthetic dataset with real color outperforms that with uniform color by 8.2 % +. • A higher than 70 % mixing proportion of synthetic data usually leads to better performance. • Synthetic point clouds can replace real ones for detecting large and flat elements. [ABSTRACT FROM AUTHOR]
ISSN:09265805
DOI:10.1016/j.autcon.2025.105963