TY - JOUR
T1 - Orienting point clouds with dipole propagation
AU - Metzer, Gal
AU - Hanocka, Rana
AU - Zorin, Denis
AU - Giryes, Raja
AU - Panozzo, Daniele
AU - Cohen-Or, Daniel
N1 - Funding Information:
We thank Shihao Wu for his helpful suggestions, and the anonymous reviewers for their constructive comments. This work is supported by the European research council (ERC-StG 757497 PI Giryes), and the Israel Science Foundation (grants no. 2366/16 and 2492/20). This work was supported in part through the NYU IT High Performance Computing resources, services, and staff expertise. This work was partially supported by the NSF CAREER award 1652515, the NSF grants IIS-1320635, DMS-1436591, DMS-1821334, OAC-1835712, OIA-1937043, CHS-1908767, CHS-1901091, a gift from Adobe Research, a gift from nTopology, and a gift from Advanced Micro Devices, Inc.
Publisher Copyright:
© 2021 ACM.
PY - 2021/7/1
Y1 - 2021/7/1
N2 - Establishing a consistent normal orientation for point clouds is a notoriously difficult problem in geometry processing, requiring attention to both local and global shape characteristics. The normal direction of a point is a function of the local surface neighborhood; yet, point clouds do not disclose the full underlying surface structure. Even assuming known geodesic proximity, calculating a consistent normal orientation requires the global context. In this work, we introduce a novel approach for establishing a globally consistent normal orientation for point clouds. Our solution separates the local and global components into two different sub-problems. In the local phase, we train a neural network to learn a coherent normal direction per patch (i.e., consistently oriented normals within a single patch). In the global phase, we propagate the orientation across all coherent patches using a dipole propagation. Our dipole propagation decides to orient each patch using the electric field defined by all previously orientated patches. This gives rise to a global propagation that is stable, as well as being robust to nearby surfaces, holes, sharp features and noise.
AB - Establishing a consistent normal orientation for point clouds is a notoriously difficult problem in geometry processing, requiring attention to both local and global shape characteristics. The normal direction of a point is a function of the local surface neighborhood; yet, point clouds do not disclose the full underlying surface structure. Even assuming known geodesic proximity, calculating a consistent normal orientation requires the global context. In this work, we introduce a novel approach for establishing a globally consistent normal orientation for point clouds. Our solution separates the local and global components into two different sub-problems. In the local phase, we train a neural network to learn a coherent normal direction per patch (i.e., consistently oriented normals within a single patch). In the global phase, we propagate the orientation across all coherent patches using a dipole propagation. Our dipole propagation decides to orient each patch using the electric field defined by all previously orientated patches. This gives rise to a global propagation that is stable, as well as being robust to nearby surfaces, holes, sharp features and noise.
KW - geometric deep learning
KW - point clouds
KW - surface reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85108507749&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85108507749&partnerID=8YFLogxK
U2 - 10.1145/3450626.3459835
DO - 10.1145/3450626.3459835
M3 - Article
AN - SCOPUS:85108507749
SN - 0730-0301
VL - 40
JO - ACM Transactions on Graphics
JF - ACM Transactions on Graphics
IS - 4
M1 - 165
ER -