Self-Supervised Large Scale Point Cloud Completion for Archaeological Site Restoration
Journal:
arXiv
Published Date:
Mar 6, 2025
Abstract
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.
