3D Dense Shape Correspondence

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.

3D Dense Shape Correspondence

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

Learning elementary structures for 3D shape generation and matching

Content

Correspondence Learning via Linearly-invariant Embedding

3D-CODED: 3D Correspondences by Deep Deformation

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

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

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

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

DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction