TY - GEN
T1 - Inception-based Deep Learning Architecture for 3D Point Cloud Completion
AU - Saffi, Houda
AU - Hmamouche, Youssef
AU - Elharrouss, Omar
AU - El Fallah Seghrouchni, Amal
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 3D point clouds are a simple and compact data format that represents the surface geometry of 3D objects. The output of the data acquisition process often yields incomplete shapes. Hence, it is crucial to infer the missing regions of 3D objects from incomplete ones for many real-world applications. By leveraging a framework of 3D point cloud completion architectures, the proposed inception module is an intermediate layer that aims to extract the hierarchical features, recognize the fine-grained details of point clouds and avoid overfitting. We conduct comprehensive experiments on three state-of-the-art datasets: ShapeNet-55, ShapeNet-34, and PCN. The experimental results demonstrate that the enhanced architectures outperform the state-of-the-art point cloud completion methods.
AB - 3D point clouds are a simple and compact data format that represents the surface geometry of 3D objects. The output of the data acquisition process often yields incomplete shapes. Hence, it is crucial to infer the missing regions of 3D objects from incomplete ones for many real-world applications. By leveraging a framework of 3D point cloud completion architectures, the proposed inception module is an intermediate layer that aims to extract the hierarchical features, recognize the fine-grained details of point clouds and avoid overfitting. We conduct comprehensive experiments on three state-of-the-art datasets: ShapeNet-55, ShapeNet-34, and PCN. The experimental results demonstrate that the enhanced architectures outperform the state-of-the-art point cloud completion methods.
UR - http://www.scopus.com/inward/record.url?scp=85143897349&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143897349&partnerID=8YFLogxK
U2 - 10.1109/AVSS56176.2022.9959483
DO - 10.1109/AVSS56176.2022.9959483
M3 - Conference contribution
AN - SCOPUS:85143897349
T3 - AVSS 2022 - 18th IEEE International Conference on Advanced Video and Signal-Based Surveillance
BT - AVSS 2022 - 18th IEEE International Conference on Advanced Video and Signal-Based Surveillance
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2022
Y2 - 29 November 2022 through 2 December 2022
ER -