利用语义UV映射改善室内场景的纹理修复
Semantic UV mapping to improve texture inpainting for indoor scenes
AI总结：
提出一种利用室内场景语义信息的UV映射预处理方法，通过结合几何与视觉特征改进UV岛与结构元素的匹配，从而提升杂物移除后的纹理修复与3D几何重建效果。
摘要：
本工作旨在改善扫描室内网格中杂物移除后的纹理修复。这通过一个新的UV映射预处理步骤实现，该步骤利用室内场景的语义信息，将UV岛与墙壁和地板等不同结构元素的3D表示更准确地匹配。语义UV映射不仅依赖几何特征，还依赖源自当前纹理的视觉特征，从而丰富了经典的UV展开算法。该分割改进了UV映射，同时简化了移除松散物体后场景的3D几何重建。每个分割元素可使用相邻元素的边界条件单独重建。由于此操作作为预处理步骤执行，未来可使用其他专门的几何和纹理重建方法进一步提升结果。
英文摘要：
This work aims to improve texture inpainting after clutter removal in scanned indoor meshes. This is achieved with a new UV mapping pre-processing step which leverages semantic information of indoor scenes to more accurately match the UV islands with the 3D representation of distinct structural elements like walls and floors. Semantic UV Mapping enriches classic UV unwrapping algorithms by not only relying on geometric features but also visual features originating from the present texture. The segmentation improves the UV mapping and simultaneously simplifies the 3D geometric reconstruction of the scene after the removal of loose objects. Each segmented element can be reconstructed separately using the boundary conditions of the adjacent elements. Because this is performed as a pre-processing step, other specialized methods for geometric and texture reconstruction can be used in the future to improve the results even further.
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