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Robust kernel-based feature representation for 3D point cloud analysis via circular convolutional networkopen access

Authors
Jung, S.Shin, Y.-G.Chung, M.
Issue Date
Jun-2023
Publisher
Academic Press Inc.
Keywords
3D point cloud analysis; Angle-based kernel convolutions; Global context aggregation; Rotation-robust point descriptor; Scale adaptation
Citation
Computer Vision and Image Understanding, v.231
Journal Title
Computer Vision and Image Understanding
Volume
231
URI
https://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/43911
DOI
10.1016/j.cviu.2023.103678
ISSN
1077-3142
1090-235X
Abstract
Feature descriptors of point clouds are used in several applications, such as registration and part segmentation of 3D point clouds. Learning representations of local geometric features is unquestionably the most important task for accurate point cloud analyses. However, it is challenging to develop rotation or scale-invariant descriptors. Most previous studies have either ignored rotations or empirically studied optimal scale parameters, which hinders the applicability of the methods for real-world datasets. In this paper, we present a new local feature description method that is robust to rotation and scale variations. Moreover, we improve representations based on a global aggregation method. First, we place kernels aligned around each point in the normal direction. To avoid the sign problem of the normal vector, we use a symmetric kernel point distribution in the tangential plane. From each kernel point, we first project the points from the spatial space to the feature space, which is robust to multiple scales and rotation, based on angles and distances. Subsequently, we perform convolutions by considering local kernel point structures and long-range global context, obtained by a global aggregation method. We experimented with our proposed descriptors on benchmark datasets (i.e., ModelNet40 and ShapeNetPart) to evaluate the performance of registration, classification, and part segmentation on 3D point clouds. Our method showed superior performances when compared to the state-of-the-art methods by reducing 70% of the rotation and translation errors in the registration task. Our method also showed comparable performance in the classification and part-segmentation tasks without any external data augmentation. © 2023 Elsevier Inc.
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