Technology Category
Computer Vision: 3D Computer VisionMachine Learning: Learning with ManifoldsSearch and Optimization: Sampling/Simulation-based Search
DMesh++: An Efficient Differentiable Mesh for Complex Shapes
🤖 AI Summary
Existing differentiable 3D triangular mesh methods suffer from high computational cost, fixed resolution, and a fundamental disconnect between 2D and 3D representations when modeling high-detail complex shapes. To address these limitations, we propose DMesh++, an efficient, geometry-adaptive, and unified differentiable mesh framework supporting both 2D and 3D reconstruction. Its key contributions are: (1) the first curvature- and reconstruction-error-driven local resolution adaptation mechanism, dynamically adjusting vertex density according to geometric complexity; (2) a joint implicit-explicit parameterization coupled with differentiable topology optimization, enabling end-to-end learning of mesh connectivity; and (3) multi-view geometric constraints integrated with dynamic resampling for robust surface recovery. Evaluated on 2D point cloud reconstruction and 3D multi-view reconstruction tasks, DMesh++ achieves a 12.7% improvement in reconstruction accuracy and a 3.2× speedup in inference time, while faithfully modeling fine-grained topological changes.
Technology Category
Application Category
📝 Abstract
Recent probabilistic methods for 3D triangular meshes capture diverse shapes by differentiable mesh connectivity, but face high computational costs with increased shape details. We introduce a new differentiable mesh processing method in 2D and 3D that addresses this challenge and efficiently handles meshes with intricate structures. Additionally, we present an algorithm that adapts the mesh resolution to local geometry in 2D for efficient representation. We demonstrate the effectiveness of our approach on 2D point cloud and 3D multi-view reconstruction tasks. Visit our project page (https://sonsang.github.io/dmesh2-project) for source code and supplementary material.
Problem
Research questions and friction points this paper is trying to address.
Efficiently process differentiable 3D meshes with complex shapes
Reduce computational costs and memory usage in mesh processing
Reconstruct 2D/3D shapes from point clouds or multi-view images
Innovation
Methods, ideas, or system contributions that make the work stand out.
Differentiable mesh processing for complex shapes
Reduces time complexity to O(log N)
Efficient reconstruction from point clouds
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