mlsanityData to 7 Oct 2026

Structured Semantic 3D Reconstruction (S23DR) Challenge 2025 -- Winning solution

arXiv 2506.16421 ↗First posted 19 Jun 2025cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 2 of its 5 references that are in the map. First posted on arXiv 19 Jun 2025.

Builds on

2 of 5 references.

References · 2

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2024Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimationcited by 389

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026SAVMap: Structure-Aided Visual Mapping of Large-Scale 2.5D Manhattan Wireframes from Panoramic Videocited by 0
  2. 2026Edge Prediction for Roof Wireframe Reconstruction with Transformerscited by 0
  3. 2026S23DR 2026: End-to-End 3D Wireframe Prediction via DETR-Style Set Prediction with Contrastive Denoisingcited by 0