SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling
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.
67e620f77203bed82eb944e9
Xianglong He
admin_assigned
2025-03-28T10:51:50.659Z
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XianglongHe
user
XianglongHe
67e620f77203bed82eb944ea
Zi-Xin Zou
admin_assigned
2025-03-28T10:52:17.598Z
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Zixin Zou
user
zouzx
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Chia-Hao Chen
67e620f77203bed82eb944ec
Yuan-Chen Guo
admin_assigned
2025-03-28T10:52:33.753Z
6346aaa3f06b237ba4e297b0
/avatars/5acb986e993eab1461200f3e9d99d022.svg
Yuan-Chen Guo
user
bennyguo
67e620f77203bed82eb944ed
Ding Liang
67e620f77203bed82eb944ee
Chun Yuan
67e620f77203bed82eb944ef
Wanli Ouyang
67e620f77203bed82eb944f0
Yan-Pei Cao
admin_assigned
2025-03-28T10:52:50.823Z
638066faf022c8a5803f7eb8
/avatars/4cfd699c3f6c5461b12b7dc5e3fe183d.svg
Yanpei Cao
user
pookiefoof
67e620f77203bed82eb944f1
Yangguang Li
admin_assigned
2025-03-28T10:52:59.331Z
64d71083a787c9bc7b9f1238
/avatars/d0b0546dec7fc5792921154bec41385a.svg
Yangguang Li
user
Lp256
SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling
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.
SparseFlex enables high-resolution 3D mesh reconstruction and generation, achieving superior accuracy and efficiency using a sparse voxel structure and rendering losses.
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