让3D补全模型生成的静态形状可任意缩放变形，保持结构完整无畸变。
Adaptive Scaling with Geometric and Visual Continuity of completed 3D objects
- 基于部件分割与可控缩放区，实现平滑插值变形
- 在Matterport3D和ShapeNet上验证，复杂形体变形更自然
- 适合需要灵活调整3D物体的场景设计与内容创作
现有3D补全网络生成的静态符号距离场（SDF）虽能精确重建几何结构，但无法自由缩放或变形，否则会产生结构失真，限制其在室内重设计、仿真和数字内容创作中的应用。本文提出一种部件感知的可调缩放框架，将静态完成的SDF转化为可编辑且结构一致的物体。从先进补全模型生成的SDF与纹理场出发，自动进行部件分割，定义用户可控的缩放区域，并对SDF、颜色和部件索引进行平滑插值，实现比例协调且无伪影的变形。进一步引入重复性策略处理大尺度变形，同时保留重复几何特征。在Matterport3D和ShapeNet上的实验表明，该方法克服了完成SDF固有的刚性缺陷，在复杂形状与重复结构上显著优于全局缩放和简单选择性缩放，视觉效果更优。
原文摘要 · Abstract (English)
Object completion networks typically produce static Signed Distance Fields (SDFs) that faithfully reconstruct geometry but cannot be rescaled or deformed without introducing structural distortions. This limitation restricts their use in applications requiring flexible object manipulation, such as indoor redesign, simulation, and digital content creation. We introduce a part-aware scaling framework that transforms these static completed SDFs into editable, structurally coherent objects. Starting from SDFs and Texture Fields generated by state-of-the-art completion models, our method performs automatic part segmentation, defines user-controlled scaling zones, and applies smooth interpolation of SDFs, color, and part indices to enable proportional and artifact-free deformation. We further incorporate a repetition-based strategy to handle large-scale deformations while preserving repeating geometric patterns. Experiments on Matterport3D and ShapeNet objects show that our method overcomes the inherent rigidity of completed SDFs and is visually more appealing than global and naive selective scaling, particularly for complex shapes and repetitive structures.
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