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.

翻译：物体补全网络通常生成静态的有符号距离场，这类表示能忠实重建几何结构，但无法在不引入结构畸变的情况下进行缩放或形变。这一局限性限制了其在需要灵活物体操控的应用中的使用，例如室内重设计、仿真和数字内容创作。我们提出一种部件感知的缩放框架，将静态的补全后SDF转换为可编辑且结构连贯的物体。该方法以先进补全模型生成的有符号距离场和纹理场为起点，执行自动部件分割，定义用户可控的缩放区域，并通过对SDF、颜色及部件索引的平滑插值实现比例协调且无伪影的形变。我们进一步引入基于重复模式的策略来处理大规模形变，同时保持重复的几何图案。在Matterport3D和ShapeNet物体上的实验表明，我们的方法克服了补全后SDF固有的刚性，相比全局缩放及简单的选择性缩放具有更优的视觉表现，尤其适用于复杂形状和重复结构。