Structured Semantic 3D Reconstruction (S23DR) Challenge 2025 -- Winning solution
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911014860095488 |
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| author | Skvrna, Jan Neumann, Lukas |
| author_facet | Skvrna, Jan Neumann, Lukas |
| contents | This paper presents the winning solution for the S23DR Challenge 2025, which involves predicting a house's 3D roof wireframe from a sparse point cloud and semantic segmentations. Our method operates directly in 3D, first identifying vertex candidates from the COLMAP point cloud using Gestalt segmentations. We then employ two PointNet-like models: one to refine and classify these candidates by analyzing local cubic patches, and a second to predict edges by processing the cylindrical regions connecting vertex pairs. This two-stage, 3D deep learning approach achieved a winning Hybrid Structure Score (HSS) of 0.43 on the private leaderboard. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16421 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structured Semantic 3D Reconstruction (S23DR) Challenge 2025 -- Winning solution Skvrna, Jan Neumann, Lukas Computer Vision and Pattern Recognition This paper presents the winning solution for the S23DR Challenge 2025, which involves predicting a house's 3D roof wireframe from a sparse point cloud and semantic segmentations. Our method operates directly in 3D, first identifying vertex candidates from the COLMAP point cloud using Gestalt segmentations. We then employ two PointNet-like models: one to refine and classify these candidates by analyzing local cubic patches, and a second to predict edges by processing the cylindrical regions connecting vertex pairs. This two-stage, 3D deep learning approach achieved a winning Hybrid Structure Score (HSS) of 0.43 on the private leaderboard. |
| title | Structured Semantic 3D Reconstruction (S23DR) Challenge 2025 -- Winning solution |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.16421 |