TY - GEN

T1 - Semantic Labeling for Point Cloud Detection and Registration Using the Universal Manifold Embedding

T2 - 22nd IEEE Statistical Signal Processing Workshop, SSP 2023

AU - Francos, Joseph M.

N1 - Publisher Copyright:
© 2023 IEEE.

PY - 2023/1/1

Y1 - 2023/1/1

N2 - Detection and registration of point cloud observations are elementary problems in 3-D vision. The Universal Manifold Embedding (UME) is a framework for mapping an observation to a matrix representation which is covariant with the rigid coordinate transformation, while its column space is invariant to the transformation. As point clouds are sets of coordinates with no functional relation imposed on them, adapting the UME framework for point cloud registration requires the definition of a function that assigns a value to each point, invariant to the action of the transformation group. Deep learning methods for point cloud semantic labeling have made it easier to incorporate semantic labels information into point cloud detection and registration. We derive analytic tools for evaluating and optimizing the UME performance in point cloud detection and registration tasks in the presence of labeling errors, when semantic labeling is employed as the transformation-invariant function defined on the point cloud.

AB - Detection and registration of point cloud observations are elementary problems in 3-D vision. The Universal Manifold Embedding (UME) is a framework for mapping an observation to a matrix representation which is covariant with the rigid coordinate transformation, while its column space is invariant to the transformation. As point clouds are sets of coordinates with no functional relation imposed on them, adapting the UME framework for point cloud registration requires the definition of a function that assigns a value to each point, invariant to the action of the transformation group. Deep learning methods for point cloud semantic labeling have made it easier to incorporate semantic labels information into point cloud detection and registration. We derive analytic tools for evaluating and optimizing the UME performance in point cloud detection and registration tasks in the presence of labeling errors, when semantic labeling is employed as the transformation-invariant function defined on the point cloud.

UR - http://www.scopus.com/inward/record.url?scp=85168909701&partnerID=8YFLogxK

U2 - 10.1109/SSP53291.2023.10208074

DO - 10.1109/SSP53291.2023.10208074

M3 - Conference contribution

AN - SCOPUS:85168909701

T3 - IEEE Workshop on Statistical Signal Processing Proceedings

SP - 21

EP - 25

BT - Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023

PB - Institute of Electrical and Electronics Engineers

Y2 - 2 July 2023 through 5 July 2023

ER -