Resonant Deformable Matching: Simultaneous Registration and Reconstruction

Corring, John; Rangarajan, Anand

doi:10.1007/978-3-319-46466-4_4

Search citation statements

Order By: Relevance

Paper Sections

Select...

Datasets and Benchmarks1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is an effective and commonly used technique to summarize and compare the accuracy of registration methods ( e.g. , RMSE [SGT*19, YDXZ20, LZXH20], geodesics [CK15, CR16, MDK*16, GFK*18, EC20]). [RBRY19] discuss benchmarks for the purposes of evaluating 3D learning‐based shape descriptors, many of which can be—and are—used for evaluating registration techniques.…”

Section: Datasets and Benchmarksmentioning

confidence: 99%

A Survey of Non‐Rigid 3D Registration

Deng

Yao

Dyke

et al. 2022

Computer Graphics Forum

View full text Add to dashboard Cite

Non-rigid registration computes an alignment between a source surface with a target surface in a non-rigid manner. In the past decade, with the advances in 3D sensing technologies that can measure time-varying surfaces, non-rigid registration has been applied for the acquisition of deformable shapes and has a wide range of applications. This survey presents a comprehensive review of non-rigid registration methods for 3D shapes, focusing on techniques related to dynamic shape acquisition and reconstruction. In particular, we review different approaches for representing the deformation field, and the methods for computing the desired deformation. Both optimization-based and learning-based methods are covered. We also review benchmarks and datasets for evaluating non-rigid registration methods, and discuss potential future research directions.

show abstract