具有几何和视觉连续性的自适应缩放完成3D对象
Adaptive Scaling with Geometric and Visual Continuity of completed 3D objects
AI总结：
本文提出一种部分感知的自适应缩放方法，通过自动分割和插值实现对完成3D对象的灵活、无伪影变形，提升复杂和重复结构的视觉效果。
摘要：
对象完成网络通常生成静态的符号距离场（SDFs），能够忠实重建几何结构，但无法进行缩放或变形而不引入结构扭曲。这种限制限制了它们在需要灵活对象操作的应用中的使用，如室内重新设计、模拟和数字内容创作。我们引入了一种部分感知的缩放框架，将这些静态完成的SDFs转换为可编辑且结构一致的对象。从由最先进的完成模型生成的SDFs和纹理场开始，我们的方法执行自动部分分割，定义用户控制的缩放区域，并应用SDFs、颜色和部分索引的平滑插值，以实现比例和无伪影的变形。我们进一步结合基于重复的策略来处理大规模变形，同时保持重复的几何图案。在Matterport3D和ShapeNet对象上的实验表明，我们的方法克服了完成SDFs的固有刚性，并且在视觉上比全局和简单的选择性缩放更吸引人，特别是在复杂形状和重复结构中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
