Yihao Luo

Yiwen Chen, Yikai Wang, Yihao Luo et al.

Meshes are the de facto 3D representation in the industry but are labor-intensive to produce. Recently, a line of research has focused on autoregressively generating meshes. This approach processes meshes into a sequence composed of vertices and then generates them vertex by vertex, similar to how a language model generates text. These methods have achieved some success but still struggle to generate complex meshes. One primary reason for this limitation is their inefficient tokenization methods. To address this issue, we introduce MeshAnything V2, an advanced mesh generation model designed to create Artist-Created Meshes that align precisely with specified shapes. A key innovation behind MeshAnything V2 is our novel Adjacent Mesh Tokenization (AMT) method. Unlike traditional approaches that represent each face using three vertices, AMT optimizes this by employing a single vertex wherever feasible, effectively reducing the token sequence length by about half on average. This not only streamlines the tokenization process but also results in more compact and well-structured sequences, enhancing the efficiency of mesh generation. With these improvements, MeshAnything V2 effectively doubles the face limit compared to previous models, delivering superior performance without increasing computational costs. We will make our code and models publicly available. Project Page: https://buaacyw.github.io/meshanything-v2/

Yihao Luo, Yikai Wang, Zhengrui Xiang et al.

In this paper, we propose the differentiable voxelization of 3D meshes via the winding number and solid angles. The proposed approach achieves fast, flexible, and accurate voxelization of 3D meshes, admitting the computation of gradients with respect to the input mesh and GPU acceleration. We further demonstrate the application of the proposed voxelization in mesh morphing, where the voxelized mesh is deformed by a neural network. The proposed method is evaluated on the ShapeNet dataset and achieves state-of-the-art performance in terms of both accuracy and efficiency.

Chenyu Zhang, Yihao Luo, Yinzhe Wu et al.

Accurate cardiac mesh reconstruction from volumetric data is essential for personalized cardiac modeling and clinical analysis. However, existing deformation-based approaches are prone to topological inconsistencies, particularly membrane penetration, which undermines the anatomical plausibility of the reconstructed mesh. To address this issue, we introduce Topology-Preserving Mesh Loss (TPM Loss), a novel loss function that explicitly enforces topological constraints during mesh deformation. By identifying topology-violating points, TPM Loss ensures spatially consistent reconstructions. Extensive experiments on CT and MRI datasets show that TPM Loss reduces topology violations by up to 93.1% while maintaining high segmentation accuracy (DSC: 89.1%-92.9%) and improving mesh fidelity (Chamfer Distance reduction up to 0.26 mm). These results demonstrate that TPM Loss effectively prevents membrane penetration and significantly improves cardiac mesh quality, enabling more accurate and anatomically consistent cardiac reconstructions.

Yihao Luo

In the field of data-driven 3D shape analysis and generation, the estimation of global topological features from localized representations such as point clouds, voxels, and neural implicit fields is a longstanding challenge. This paper introduces a novel, differentiable algorithm tailored to accurately estimate the global topology of 3D shapes, overcoming the limitations of traditional methods rooted in mesh reconstruction and topological data analysis. The proposed method ensures high accuracy, efficiency, and instant computation with GPU compatibility. It begins with an efficient calculation of the self-adjoint Weingarten map for point clouds and its adaptations for other modalities. The curvatures are then extracted, and their integration over tangent differentiable Voronoi elements is utilized to estimate key topological invariants, including the Euler number and Genus. Additionally, an auto-optimization mechanism is implemented to refine the local moving frames and area elements based on the integrity of topological invariants. Experimental results demonstrate the method's superior performance across various datasets. The robustness and differentiability of the algorithm ensure its seamless integration into deep learning frameworks, offering vast potential for downstream tasks in 3D shape analysis.