A new multi-camera dataset and a transformer-based method reconstruct a dense 3D rat body surface from 10 sparse keypoints, with reported mean errors around 5 to 7 mm.
A Survey of Non-Rigid 3D Registration
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
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.
citation-role summary
citation-polarity summary
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
RatBodyFormer: Rat Body Surface from Keypoints
A new multi-camera dataset and a transformer-based method reconstruct a dense 3D rat body surface from 10 sparse keypoints, with reported mean errors around 5 to 7 mm.