AI RESEARCH
SIMPC: Learning Self-Induced Mirror-Point Consistency for Unsupervised Point Cloud Denoising
arXiv CS.CV
•
ArXi:2605.26894v1 Announce Type: new In point clouds, noise directly perturbs point coordinates that encode both spatial location and geometry, making one-to-one correspondence construction challenging than in images. Existing methods impose statistical mappings across noisy variants via noise or optimal transport, but suffer from correspondence ambiguity. In this work, we propose Self-Induced Mirror-Point Consistency (SIMPC) to learn deterministic correspondences between points and the underlying surface in an unsupervised manner.