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.
