mlsanityData to 7 Oct 2026

3D Neural Edge Reconstruction

arXiv 2405.19295 ↗First posted 29 May 2024cs.CVcited by 12

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Abstract (arXiv)

Real-world objects and environments are predominantly composed of edge features, including straight lines and curves. Such edges are crucial elements for various applications, such as CAD modeling, surface meshing, lane mapping, etc. However, existing traditional methods only prioritize lines over curves for simplicity in geometric modeling. To this end, we introduce EMAP, a new method for learning 3D edge representations with a focus on both lines and curves. Our method implicitly encodes 3D edge distance and direction in Unsigned Distance Functions (UDF) from multi-view edge maps. On top of this neural representation, we propose an edge extraction algorithm that robustly abstracts parametric 3D edges from the inferred edge points and their directions. Comprehensive evaluations demonstrate that our method achieves better 3D edge reconstruction on multiple challenging datasets. We further show that our learned UDF field enhances neural surface reconstruction by capturing more details.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 54 of its 76 references that are in the map. First posted on arXiv 29 May 2024.

Builds on

54 of 76 references.

References · 54

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38 more
  1. 2020Convolutional Occupancy Networkscited by 760
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 3.

12 papers.