title: SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling
summary: Creating high-fidelity 3D meshes with arbitrary topology, including open
  surfaces and complex interiors, remains a significant challenge. Existing
  implicit field methods often require costly and detail-degrading watertight
  conversion, while other approaches struggle with high resolutions. This paper
  introduces SparseFlex, a novel sparse-structured isosurface representation that
  enables differentiable mesh reconstruction at resolutions up to 1024^3
  directly from rendering losses. SparseFlex combines the accuracy of Flexicubes
  with a sparse voxel structure, focusing computation on surface-adjacent regions
  and efficiently handling open surfaces. Crucially, we introduce a frustum-aware
  sectional voxel training strategy that activates only relevant voxels during
  rendering, dramatically reducing memory consumption and enabling
  high-resolution training. This also allows, for the first time, the
  reconstruction of mesh interiors using only rendering supervision. Building
  upon this, we demonstrate a complete shape modeling pipeline by training a
  variational autoencoder (VAE) and a rectified flow transformer for high-quality
  3D shape generation. Our experiments show state-of-the-art reconstruction
  accuracy, with a ~82% reduction in Chamfer Distance and a ~88% increase in
  F-score compared to previous methods, and demonstrate the generation of
  high-resolution, detailed 3D shapes with arbitrary topology. By enabling
  high-resolution, differentiable mesh reconstruction and generation with
  rendering losses, SparseFlex significantly advances the state-of-the-art in 3D
  shape representation and modeling.
paper_authors._id: 67e620f77203bed82eb944e9
paper_authors.name: Xianglong He
paper_authors.status: admin_assigned
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paper_authors.fullname: XianglongHe
paper_authors.type: user
paper_authors.user: XianglongHe
paper_authors._id: 67e620f77203bed82eb944ea
paper_authors.name: Zi-Xin Zou
paper_authors.status: admin_assigned
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paper_authors.fullname: Zixin Zou
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paper_authors.user: zouzx
paper_authors._id: 67e620f77203bed82eb944eb
paper_authors.name: Chia-Hao Chen
paper_authors._id: 67e620f77203bed82eb944ec
paper_authors.name: Yuan-Chen Guo
paper_authors.status: admin_assigned
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paper_authors._id: 6346aaa3f06b237ba4e297b0
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paper_authors.fullname: Yuan-Chen Guo
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paper_authors.user: bennyguo
paper_authors._id: 67e620f77203bed82eb944ed
paper_authors.name: Ding Liang
paper_authors._id: 67e620f77203bed82eb944ee
paper_authors.name: Chun Yuan
paper_authors._id: 67e620f77203bed82eb944ef
paper_authors.name: Wanli Ouyang
paper_authors._id: 67e620f77203bed82eb944f0
paper_authors.name: Yan-Pei Cao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T10:52:50.823Z
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paper_authors.avatarUrl: /avatars/4cfd699c3f6c5461b12b7dc5e3fe183d.svg
paper_authors.fullname: Yanpei Cao
paper_authors.type: user
paper_authors.user: pookiefoof
paper_authors._id: 67e620f77203bed82eb944f1
paper_authors.name: Yangguang Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T10:52:59.331Z
paper_authors._id: 64d71083a787c9bc7b9f1238
paper_authors.avatarUrl: /avatars/d0b0546dec7fc5792921154bec41385a.svg
paper_authors.fullname: Yangguang Li
paper_authors.type: user
paper_authors.user: Lp256
paper_title: SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling
paper_summary: Creating high-fidelity 3D meshes with arbitrary topology, including open
  surfaces and complex interiors, remains a significant challenge. Existing
  implicit field methods often require costly and detail-degrading watertight
  conversion, while other approaches struggle with high resolutions. This paper
  introduces SparseFlex, a novel sparse-structured isosurface representation that
  enables differentiable mesh reconstruction at resolutions up to 1024^3
  directly from rendering losses. SparseFlex combines the accuracy of Flexicubes
  with a sparse voxel structure, focusing computation on surface-adjacent regions
  and efficiently handling open surfaces. Crucially, we introduce a frustum-aware
  sectional voxel training strategy that activates only relevant voxels during
  rendering, dramatically reducing memory consumption and enabling
  high-resolution training. This also allows, for the first time, the
  reconstruction of mesh interiors using only rendering supervision. Building
  upon this, we demonstrate a complete shape modeling pipeline by training a
  variational autoencoder (VAE) and a rectified flow transformer for high-quality
  3D shape generation. Our experiments show state-of-the-art reconstruction
  accuracy, with a ~82% reduction in Chamfer Distance and a ~88% increase in
  F-score compared to previous methods, and demonstrate the generation of
  high-resolution, detailed 3D shapes with arbitrary topology. By enabling
  high-resolution, differentiable mesh reconstruction and generation with
  rendering losses, SparseFlex significantly advances the state-of-the-art in 3D
  shape representation and modeling.
paper_ai_summary: SparseFlex enables high-resolution 3D mesh reconstruction and generation, achieving superior accuracy and efficiency using a sparse voxel structure and rendering losses.
paper_ai_keywords: sparse-structured isosurface representation differentiable mesh reconstruction Flexicubes sparse voxel structure frustum-aware sectional voxel training strategy rendering supervision variational autoencoder (VAE) rectified flow transformer Chamfer Distance F-score 3D shape representation 3D shape modeling