无需预对齐和手工特征，实现旋转不变的3D形状密集对应
RINO: Rotation-Invariant Non-Rigid Correspondences
- 基于SO(3)不变向量学习与方向感知复函数映射，直接从原始几何提取特征
- 在任意姿态、非等距变形、部分数据等挑战场景下性能超越现有方法
- 适合需要高鲁棒性3D匹配的科研与工业应用，如形变建模与三维识别
稠密3D形状对应仍是计算机视觉与图形学中的核心挑战，因许多深度学习方法仍依赖中间几何特征或手工描述子，难以应对非等距变形、部分数据及非流形输入。为此，我们提出RINO——一种无监督、旋转不变的稠密对应框架，有效统一刚体与非刚体形状匹配。其核心是新型RINONet，通过结合基于向量的SO(3)不变学习与方向感知的复函数映射，直接从原始几何中提取鲁棒特征，实现端到端数据驱动，无需形状预对齐或手工特征。大量实验表明，RINO在包括任意姿态、非等距、部分性、非流形性和噪声在内的挑战性非刚性匹配任务中表现空前优越。
原文摘要 · Abstract (English)
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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