RINO: Rotation-Invariant Non-Rigid Correspondences

• URL: http://arxiv.org/abs/2603.27773v1
• Date: Sun, 29 Mar 2026 17:11:57 GMT
• Title: RINO: Rotation-Invariant Non-Rigid Correspondences
• Abstract summary: RINO is an unsupervised, rotation-invariant dense correspondence framework.<n>RINO unifies rigid and non-rigid shape matching.<n>Experiments show unprecedented performance of RINO across challenging non-rigid matching tasks.
• Score: 50.628820523204304
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Dense 3D shape correspondence remains a central challenge in computer vision and graphics as many deep learning approaches still rely on intermediate geometric features or handcrafted descriptors, limiting their effectiveness under non-isometric deformations, partial data, and non-manifold inputs. To overcome these issues, we introduce RINO, an unsupervised, rotation-invariant dense correspondence framework that effectively unifies rigid and non-rigid shape matching. The core of our method is the novel RINONet, a feature extractor that integrates vector-based SO(3)-invariant learning with orientation-aware complex functional maps to extract robust features directly from raw geometry. This allows for a fully end-to-end, data-driven approach that bypasses the need for shape pre-alignment or handcrafted features. Extensive experiments show unprecedented performance of RINO across challenging non-rigid matching tasks, including arbitrary poses, non-isometry, partiality, non-manifoldness, and noise.

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