SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling (2503.21732v1)

Abstract: 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: High-Resolution and Arbitrary-Topology 3D Shape Modeling

The paper "SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling" introduces a new methodology for high-fidelity 3D shape modeling, particularly emphasizing the creation of 3D meshes with arbitrary topology that includes complex geometries and open surfaces. One of the significant hurdles in the domain of 3D modeling has been the ability to generate detailed and high-resolution 3D shapes without significant computational costs or the degradation of surface details. SparseFlex addresses these challenges through a unique sparse-structured isosurface representation that enables efficient processing and memory usage, supporting resolutions up to $1024^3$.

Methodological Advancements

SparseFlex builds upon and refines existing solutions such as Flexicubes by integrating a sparse voxel-based structure, moving away from traditional dense grids that are both memory-intensive and computationally expensive. This sparse structure focuses computational resources on areas adjacent to the surface, thereby efficiently handling open surfaces without the typical overhead found in dense grid methods.

The paper introduces a novel training approach termed "frustum-aware sectional voxel training." This strategy selectively activates voxels only within the camera's viewing frustum during training, which significantly trims down memory requirements and computational load. As a result, the approach facilitates high-resolution mesh extraction and surface detail preservation using rendering losses, allowing for the reconstruction of both exteriors and interiors of the 3D model.

Numerical and Comparative Results

SparseFlex demonstrates superior numerical performance compared to existing frameworks. The authors report a substantial decrease in the Chamfer Distance metric by approximately 82%, and an increase in the F-score by around 88% compared to other state-of-the-art techniques. These improvements signify an enhanced capability in accurately reconstructing 3D surfaces and preserving geometric details, especially in models featuring complex topology.

Furthermore, the SparseFlex approach does not rely on watertight conversions for implicit field methods, which commonly degrade detailing. Instead, it effectively bypasses this step through the direct optimization provided by rendering losses. This process also introduces the ability to model interior surfaces under rendering supervision, a noteworthy capability absent in previous models heavily reliant on watertight representations.

Implications and Future Directions

The implementation of SparseFlex heralds substantial implications for sectors reliant on 3D shape generation, including entertainment, virtual reality, robotics, and design. The methodological refinements allow practitioners to generate high-quality 3D models more efficiently while maintaining detail integrity—a leap forward for applications requiring intricate component simulations or detailed digital twins.

In theoretical terms, SparseFlex provides a robust framework for further research into sparse representations in 3D modeling, rendering novel opportunities for exploration in unsupervised learning scenarios or real-time neural rendering. Future research directions could build upon these concepts, focusing on enhancing the adaptability of SparseFlex representations to broader contexts, such as dynamic scenes or interaction-rich environments.

In conclusion, the SparseFlex model makes significant strides in the domain of 3D shape modeling, emphasizing efficiency without compromising the resolution or topological complexity. This paper’s approach and results may serve as a catalyst for upcoming developments in high-fidelity 3D shape generation, setting the stage for future research and applications.