论文标题
部分3D数据的纹理完成的隐式特征网络
Implicit Feature Networks for Texture Completion from Partial 3D Data
论文作者
论文摘要
推断3D纹理的事先工作要么使用纹理图谱，要么需要紫外线映射，因此具有不连续性或彩色体素，它们的内存效率低下且分辨率有限。最近的工作，可以预测每个XYZ坐标形成纹理字段的RGB颜色，但专注于完成纹理，给定一个2D图像。相反，我们将重点放在局部和不完整的3D扫描中的3D纹理和几何结束上。如果网络最近使用多尺度的深度特征编码实现了3D几何完成的最新结果，但是输出缺乏纹理。在这项工作中，我们将If-Nets推广到从人类和任意对象的部分纹理扫描中完成纹理完成。我们的关键见解是，从3D部分纹理和完成的几何形状中提取的本地和全球深度功能纳入了3D纹理完成。具体而言，鉴于局部3D纹理和3D几何形状使用IF-NET完成，我们的模型成功地将缺失的纹理零件与完整的几何形状一致。我们的模型赢得了敏锐的ECCV'20挑战，在所有挑战中都取得了最高的表现。
Prior work to infer 3D texture use either texture atlases, which require uv-mappings and hence have discontinuities, or colored voxels, which are memory inefficient and limited in resolution. Recent work, predicts RGB color at every XYZ coordinate forming a texture field, but focus on completing texture given a single 2D image. Instead, we focus on 3D texture and geometry completion from partial and incomplete 3D scans. IF-Nets have recently achieved state-of-the-art results on 3D geometry completion using a multi-scale deep feature encoding, but the outputs lack texture. In this work, we generalize IF-Nets to texture completion from partial textured scans of humans and arbitrary objects. Our key insight is that 3D texture completion benefits from incorporating local and global deep features extracted from both the 3D partial texture and completed geometry. Specifically, given the partial 3D texture and the 3D geometry completed with IF-Nets, our model successfully in-paints the missing texture parts in consistence with the completed geometry. Our model won the SHARP ECCV'20 challenge, achieving highest performance on all challenges.
