EC-Net: an Edge-aware Point set Consolidation Network
Refereed conference paper presented and published in conference proceedings


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AbstractPoint clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required to be consolidated. In this paper, we present the first deep learning based edge-aware technique to facilitate the consolidation of point clouds. We design our network to process points grouped in local patches, and train it to learn and help consolidate points, deliberately for edges. To achieve this, we formulate a regression component to simultaneously recover 3D point coordinates and point-to-edge distances from upsampled features, and an edge-aware joint loss function to directly minimize distances from output points to 3D meshes and to edges. Compared with previous neural network based works, our consolidation is edge-aware. During the synthesis, our network can attend to the detected sharp edges and enable more accurate 3D reconstructions. Also, we trained our network on virtual scanned point clouds, demonstrated the performance of our method on both synthetic and real point clouds, presented various surface reconstruction results, and showed how our method outperforms the state-of-the-arts.
All Author(s) ListLequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng
Name of Conference15th European Conference on Computer Vision, ECCV 2018
Start Date of Conference08/09/2018
End Date of Conference14/09/2018
Place of ConferenceMunich
Country/Region of ConferenceGermany
Proceedings TitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Year2018
Volume Number11211
PublisherSpringer
Pages398 - 414
ISBN978-303001233-5
ISSN0302-9743
LanguagesEnglish-United States
Keywordspoint cloud, learning, neural network, edge-aware

Last updated on 2020-21-05 at 02:11