TY - GEN
T1 - Point MixSwap
T2 - 17th European Conference on Computer Vision, ECCV 2022
AU - Umam, Ardian
AU - Yang, Cheng Kun
AU - Chuang, Yung Yu
AU - Chuang, Jen Hui
AU - Lin, Yen Yu
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Data augmentation is developed for increasing the amount and diversity of training data to enhance model learning. Compared to 2D images, point clouds, with the 3D geometric nature as well as the high collection and annotation costs, pose great challenges and potentials for augmentation. This paper presents a 3D augmentation method that explores the structural variance across multiple point clouds, and generates more diverse point clouds to enrich the training set. Specifically, we propose an attention module that decomposes a point cloud into several disjoint point subsets, called divisions, in a way where each division has a corresponding division in another point cloud. The augmented point clouds are synthesized by swapping matched divisions. They exhibit high diversity since both intra- and inter-cloud variations are explored, hence useful for downstream tasks. The proposed method for augmentation can act as a module and be integrated into a point-based network. The resultant framework is end-to-end trainable. The experiments show that it achieves state-of-the-art performance on the ModelNet40 and ModelNet10 benchmarks. The code for this work is publicly available (The source code is available at: https://github.com/ardianumam/PointMixSwap ).
AB - Data augmentation is developed for increasing the amount and diversity of training data to enhance model learning. Compared to 2D images, point clouds, with the 3D geometric nature as well as the high collection and annotation costs, pose great challenges and potentials for augmentation. This paper presents a 3D augmentation method that explores the structural variance across multiple point clouds, and generates more diverse point clouds to enrich the training set. Specifically, we propose an attention module that decomposes a point cloud into several disjoint point subsets, called divisions, in a way where each division has a corresponding division in another point cloud. The augmented point clouds are synthesized by swapping matched divisions. They exhibit high diversity since both intra- and inter-cloud variations are explored, hence useful for downstream tasks. The proposed method for augmentation can act as a module and be integrated into a point-based network. The resultant framework is end-to-end trainable. The experiments show that it achieves state-of-the-art performance on the ModelNet40 and ModelNet10 benchmarks. The code for this work is publicly available (The source code is available at: https://github.com/ardianumam/PointMixSwap ).
UR - http://www.scopus.com/inward/record.url?scp=85142686723&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-19818-2_34
DO - 10.1007/978-3-031-19818-2_34
M3 - Conference contribution
AN - SCOPUS:85142686723
SN - 9783031198175
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 596
EP - 611
BT - Computer Vision – ECCV 2022 - 17th European Conference, Proceedings
A2 - Avidan, Shai
A2 - Brostow, Gabriel
A2 - Cissé, Moustapha
A2 - Farinella, Giovanni Maria
A2 - Hassner, Tal
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 23 October 2022 through 27 October 2022
ER -