用多视角投影图像自监督修复缺失的考古遗址点云。
Self-Supervised Large Scale Point Cloud Completion for Archaeological Site Restoration
- 将点云转为多中心投影图像，通过图像补全还原3D结构。
- 在600多个不完整、分布不均的遗址上实现高保真重建。
- 适合考古数字化、文化遗产修复等场景使用。
点云补全是恢复因遮挡而缺失的不完整点云的重要手段。现有自监督方法在大尺度物体缺失表面且点分布不均时难以生成高保真结果。本文提出一种新方法，仅需感兴趣区域的粗略边界标注，将原始点云投影至多中心投影（MCOP）图像，包含5个通道（RGB、深度、旋转）。点云补全转化为对MCOP图像中缺失像素的图像修复。针对结构不完整和现有部分分布不均的问题，设计自监督方案，学习用类似“完整”区域的点补全图像。引入特殊损失函数增强补全图像的规则性和一致性，并映射回3D生成最终修复结果。大量实验表明，该方法在秘鲁600多个不完整且分布不均的考古遗址上表现优异。
原文摘要 · Abstract (English)
Point cloud completion helps restore partial incomplete point clouds suffering occlusions. Current self-supervised methods fail to give high fidelity completion for large objects with missing surfaces and unbalanced distribution of available points. In this paper, we present a novel method for restoring large-scale point clouds with limited and imbalanced ground-truth. Using rough boundary annotations for a region of interest, we project the original point clouds into a multiple-center-of-projection (MCOP) image, where fragments are projected to images of 5 channels (RGB, depth, and rotation). Completion of the original point cloud is reduced to inpainting the missing pixels in the MCOP images. Due to lack of complete structures and an unbalanced distribution of existing parts, we develop a self-supervised scheme which learns to infill the MCOP image with points resembling existing "complete" patches. Special losses are applied to further enhance the regularity and consistency of completed MCOP images, which is mapped back to 3D to form final restoration. Extensive experiments demonstrate the superiority of our method in completing 600+ incomplete and unbalanced archaeological structures in Peru.
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