用2D预测掩码，3D扩散模型修复损毁文物的形状与颜色。
Mask-Conditioned Voxel Diffusion for Joint Geometry and Color Inpainting
- 先用2D网络在体素切片上预测损伤区域，生成三维掩码。
- 32^3分辨率下，修复后几何更完整、色彩更连贯，优于对称基线方法。
- 适合文化遗产数字化修复，尤其关注形状与颜色联合重建的场景。
我们提出一种轻量级两阶段框架，用于受损3D物体的联合几何与颜色修复，灵感来自文化遗产数字化修复。流程将损伤定位与重建分离：第一阶段通过2D卷积网络在体素化物体提取的RGB切片上预测损伤掩码，并聚合为体素掩码；第二阶段采用基于扩散的3D U-Net，在体素网格上进行掩码条件修复，同时重建几何与颜色并保留已知区域。模型通过联合优化目标（包含体素占据重建、掩码颜色重建与感知正则化）同时预测占据关系与颜色。我们在合成损坏的纹理文物数据集上使用标准几何与颜色指标评估。相比对称基线方法，本方法在固定32^3分辨率下生成更完整的几何结构和更一致的颜色重建。结果表明，显式掩码条件是引导体素扩散模型实现联合3D几何与颜色修复的有效策略。
原文摘要 · Abstract (English)
We present a lightweight two-stage framework for joint geometry and color inpainting of damaged 3D objects, motivated by the digital restoration of cultural heritage artifacts. The pipeline separates damage localization from reconstruction. In the first stage, a 2D convolutional network predicts damage masks on RGB slices extracted from a voxelized object, and these predictions are aggregated into a volumetric mask. In the second stage, a diffusion-based 3D U-Net performs mask-conditioned inpainting directly on voxel grids, reconstructing geometry and color while preserving observed regions. The model jointly predicts occupancy and color using a composite objective that combines occupancy reconstruction with masked color reconstruction and perceptual regularization. We evaluate the approach on a curated set of textured artifacts with synthetically generated damage using standard geometric and color metrics. Compared to symmetry-based baselines, our method produces more complete geometry and more coherent color reconstructions at a fixed 32^3 resolution. Overall, the results indicate that explicit mask conditioning is a practical way to guide volumetric diffusion models for joint 3D geometry and color inpainting.
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