用户参与式生成3D模型分割图谱，支持游戏与元宇宙内容制作。
Human-in-the-Loop Atlas-Based 3D Asset Segmentation for Interactive Content Workflows
- 通过贪婪覆盖策略选视图，结合SAM2与标注工具交互分割
- 生成统一分割图谱，支持材质分配、风格迁移等下游任务
- 适用于文化遗产等复杂几何的交互式内容生产
将3D资产按语义区域分割仍具挑战性，尤其在应用依赖且需用户干预时。本文提出一种人机协同流程，从3D模型生成可用于交互媒体、游戏和XR内容工作流的2D参数化图谱。方法首先采用贪心集合覆盖策略，基于采样表面点选择紧凑视图集；随后利用SAM2与Label Studio对视图进行交互分割。生成的掩码回投影至模型的UV参数化空间，形成统一的分割图谱，支持后续任务如分区域材质分配、风格迁移与语义标注。通过在8个文化遗产对象上的示范性技术评估验证该流程。结果表明，该方法可在多种几何形态下生成可用的分割图谱，并揭示了人工修正的主要来源：精细结构、凹陷区域及弱外观边界。代码已开源：https://github.com/saptarshineil/ai_assisted_atlas_segmentation
原文摘要 · Abstract (English)
Segmenting 3D assets into meaningful regions remains challenging, especially when segmentation criteria are application-dependent and require user control. We present a human-in-the-loop pipeline for generating a segmented 2D parameterized atlas from a 3D model for interactive media, game, and XR content workflows. Our method first selects a compact set of rendered views using a greedy set cover strategy over sampled surface points, and then supports interactive segmentation of these views with SAM~2 and Label Studio. The resulting masks are back-projected onto the model's UV parameterization to produce a unified segmented atlas that supports downstream production tasks such as segment-wise material assignment, style transfer, and semantic labeling. We assess the pipeline through a demonstration-based technical evaluation on eight cultural heritage objects. The results show that the approach can generate usable segmented atlases across diverse geometries while revealing recurring sources of manual correction, particularly fine structures, cavities, and weak appearance boundaries. The code is available at https://github.com/saptarshineil/ai_assisted_atlas_segmentation
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