VisDiff: SDF-Guided Polygon Generation for Visibility Reconstruction and Recognition

• URL: http://arxiv.org/abs/2410.05530v1
• Date: Mon, 7 Oct 2024 22:17:11 GMT
• Title: VisDiff: SDF-Guided Polygon Generation for Visibility Reconstruction and Recognition
• Authors: Rahul Moorthy, Volkan Isler,
• Abstract summary: An active frontier in representation learning is understanding representations for structures which may not admit well-behaved local neighborhoods or distance functions. We introduce VisDiff, a novel diffusion-based approach to reconstruct a polygon from its given visibility graph G. Our method first estimates the signed distance function (SDF) of P from G. Afterwards, it extracts ordered locations that have the pairwise visibility relationship given by the edges of G.
• Score: 23.45542550861476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The capability to learn latent representations plays a key role in the effectiveness of recent machine learning methods. An active frontier in representation learning is understanding representations for combinatorial structures which may not admit well-behaved local neighborhoods or distance functions. For example, for polygons, slightly perturbing vertex locations might lead to significant changes in their combinatorial structure and may even lead to invalid polygons. In this paper, we investigate representations to capture the underlying combinatorial structures of polygons. Specifically, we study the open problem of Visibility Reconstruction: Given a visibility graph G, construct a polygon P whose visibility graph is G. We introduce VisDiff, a novel diffusion-based approach to reconstruct a polygon from its given visibility graph G. Our method first estimates the signed distance function (SDF) of P from G. Afterwards, it extracts ordered vertex locations that have the pairwise visibility relationship given by the edges of G. Our main insight is that going through the SDF significantly improves learning for reconstruction. In order to train VisDiff, we make two main contributions: (1) We design novel loss components for computing the visibility in a differentiable manner and (2) create a carefully curated dataset. We use this dataset to benchmark our method and achieve 21% improvement in F1-Score over standard methods. We also demonstrate effective generalization to out-of-distribution polygon types and show that learning a generative model allows us to sample the set of polygons with a given visibility graph. Finally, we extend our method to the related combinatorial problem of reconstruction from a triangulation. We achieve 95% classification accuracy of triangulation edges and a 4% improvement in Chamfer distance compared to current architectures.

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