Searching point patterns in point clouds describing local topography
By: Ewa Bednarczuk , Rafał Bieńkowski , Robert Kłopotek and more
Potential Business Impact:
Matches 3D shapes by looking at their bumpy parts.
We address the problem of comparing and aligning spatial point configurations in $\mathbb{R}^3$ arising from structured geometric patterns. Each pattern is decomposed into arms along which we define a normalized finite-difference operator measuring local variations of the height component with respect to the planar geometry of the pattern. This quantity provides a parametrization-independent local descriptor that complements global similarity measures. In particular, it integrates naturally with Wasserstein-type distances for comparing point distributions and with Procrustes analysis for rigid alignment of geometric structures.
Similar Papers
Enhanced 3D Shape Analysis via Information Geometry
CV and Pattern Recognition
Compares 3D shapes accurately and reliably.
Advancing Precision in Multi-Point Cloud Fusion Environments
CV and Pattern Recognition
Finds tiny flaws on factory parts faster.
Manifold-Aware Point Cloud Completion via Geodesic-Attentive Hierarchical Feature Learning
CV and Pattern Recognition
Makes 3D shapes look complete and real.