P2Voxel: Pyramid Pivot Voxelization for 3D Mesh Tokenization

• URL: http://arxiv.org/abs/2608.07549v1
• Date: Sat, 01 Aug 2026 13:04:29 GMT
• Title: P2Voxel: Pyramid Pivot Voxelization for 3D Mesh Tokenization
• Abstract summary: We introduce textbfP2Voxel, a pyramid pivot voxelization framework for compact and reconstruction-aware mesh tokenization.<n>Under the textitSpatial Complexity assumption, Pyramid Pivot Voxelization exploits the spatial non-uniformity of real surfaces.<n>Under the textitBlock Reconstructability assumption, a Pyramid VAE learns compact multi-resolution latent codes over locally reconstructable pivot blocks.
• Score: 60.385793205570046
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Triangle meshes provide explicit and accurate surface geometry, yet their irregular topology connectivity makes 3D mesh tokenization a geometric sampling problem: how to sample and organize geometric evidence into compact, structured and learnable tokens. Beyond field-centric volumetric sampling and edge-intersection surface sampling, we retarget mesh tokenization as \textit{local surface evidence sampling}: identifying the minimal geometric evidence inside each active voxel that is sufficient for deterministic surface recovery. To this end, we introduce \textbf{P2Voxel}, a pyramid pivot voxelization framework for compact and reconstruction-aware mesh tokenization. P2Voxel is built on three key innovations. Under the \textit{Local Planarity} assumption, Pivot Voxelization represents each active voxel with a surface pivot and an orientation sign, providing minimal local evidence that can induce the corner values required for deterministic reconstruction. Under the \textit{Spatial Complexity} assumption, Pyramid Pivot Voxelization exploits the spatial non-uniformity of real surfaces by allocating finer pivot tokens to geometrically complex regions while keeping smooth regions coarse and compact. Under the \textit{Block Reconstructability} assumption, a Pyramid VAE learns compact multi-resolution latent codes over locally reconstructable pivot blocks, avoiding the need to model the entire high-resolution voxelized shape as a dense global field. Together, these designs convert meshes into compact, structured, and learnable pyramid pivot tokens, enabling efficient mesh reconstruction for downstream 3D tasks.

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