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3D Dense Shape Correspondence

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보존된 평가 기록: 11

Finding a meaningful correspondence between two or more shapes is one of the most fundamental shape analysis tasks. The problem can be generally stated as: given input shapes S1,S2,...,SN, find a meaningful relation (or mapping) between their elements. Under different contexts, the problem has also been referred to as registration, alignment, or simply, matching. Shape correspondence is a key algorithmic component in tasks such as 3D scan alignment and space-time reconstruction, as well as an indispensable prerequisite in diverse applications including attribute transfer, shape interpolation, and statistical modeling.

논문

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3D Dense Shape Correspondence

논문

Unsupervised Template-assisted Point Cloud Shape Correspondence Network

Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features

SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence

Anisotropic Multi-Scale Graph Convolutional Network for Dense Shape Correspondence

ROCA: Robust CAD Model Retrieval and Alignment from a Single Image

DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction

CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds

Correspondence Learning via Linearly-invariant Embedding

3D Meta Point Signature: Learning to Learn 3D Point Signature for 3D Dense Shape Correspondence

Learning elementary structures for 3D shape generation and matching

3D-CODED : 3D Correspondences by Deep Deformation