mlsanityData to 7 Oct 2026

Differentiable Convex Polyhedra Optimization from Multi-view Images

arXiv 2407.15686 ↗First posted 22 Jul 2024cs.GR cs.CVcited by 1

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Abstract (arXiv)

This paper presents a novel approach for the differentiable rendering of convex polyhedra, addressing the limitations of recent methods that rely on implicit field supervision. Our technique introduces a strategy that combines non-differentiable computation of hyperplane intersection through duality transform with differentiable optimization for vertex positioning with three-plane intersection, enabling gradient-based optimization without the need for 3D implicit fields. This allows for efficient shape representation across a range of applications, from shape parsing to compact mesh reconstruction. This work not only overcomes the challenges of previous approaches but also sets a new standard for representing shapes with convex polyhedra.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 36 of its 54 references that are in the map. First posted on arXiv 22 Jul 2024.

Builds on

36 of 54 references.

References · 36

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24 more
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Citing papers per year: 2024: 1.

1 paper.

  1. 20243D Convex Splatting: Radiance Field Rendering with 3D Smooth Convexescited by 50