mlsanityData to 14 Oct 2026

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

arXiv 2412.19145 ↗First posted 26 Dec 2024cs.CVcited by 0

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Abstract (arXiv)

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 (for building large models for point clouds).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 39 references that are in the map. First posted on arXiv 26 Dec 2024.

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16 of 39 references.

References · 16

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  3. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,117
  4. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  5. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  6. 2020Point Transformercited by 1,470
  7. 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,208
  8. 2020PCT: Point cloud transformercited by 732
  9. 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
  10. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 585
  11. 2019Deep Learning for 3D Point Clouds: A Surveycited by 558
  12. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 388
4 more
  1. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  2. 2019Learning from Synthetic Data for Crowd Counting in the Wildcited by 148
  3. 2022SODA: Site Object Detection dAtaset for Deep Learning in Constructioncited by 6
  4. 2023Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR Datacited by 2

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