P2Voxel: New Method for Compact 3D Mesh Tokenization
A novel approach for tokenizing 3D meshes, named P2Voxel, has been detailed in a paper on arXiv (ID: 2608.07549). This cross-announcement outlines a technique that transforms irregular triangle meshes into compact, structured, and learnable tokens. The fundamental concept involves viewing mesh tokenization as a geometric sampling challenge, specifically 'local surface evidence sampling', which seeks to pinpoint the essential geometric information within each active voxel needed for deterministic surface recovery. P2Voxel incorporates three significant innovations, such as the 'Local Planarity' assumption and 'Pivot Voxelization', which defines each active voxel with a surface pivot and orientation sign. This method offers minimal local evidence to derive the corner values necessary for deterministic surface recovery. The paper can be accessed via the provided URL and is pertinent to 3D graphics, computer vision, and machine learning, as it tackles the representation of 3D surfaces for deep learning applications.
Key facts
- P2Voxel is a pyramid pivot voxelization framework for compact and reconstruction-aware mesh tokenization.
- The paper is available on arXiv with ID 2608.07549.
- The method treats mesh tokenization as a geometric sampling problem.
- It introduces the concept of 'local surface evidence sampling'.
- The framework is built on three key innovations, including the Local Planarity assumption and Pivot Voxelization.
- Pivot Voxelization represents each active voxel with a surface pivot and an orientation sign.
- The approach aims to provide minimal local evidence for deterministic surface recovery.
- The paper is a cross-announcement type on arXiv.
Entities
Institutions
- arXiv