RegGeoNet : Learning Regular Representations for Large-Scale 3D Point Clouds
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 3100–3122 |
Number of pages | 23 |
Journal / Publication | International Journal of Computer Vision |
Volume | 130 |
Issue number | 12 |
Online published | 27 Sept 2022 |
Publication status | Published - Dec 2022 |
Link(s)
Abstract
Deep learning has proven an effective tool for 3D point cloud processing. Currently, most deep set architectures are developed for sparse inputs (typically with a few thousand points), which are unable to provide sufficient structural statistics and semantic cues due to low resolutions. Since these architectures suffer from unacceptable computational and memory costs when consuming dense inputs, there is a pressing need in real-world applications to handle large-scale 3D point clouds. To bridge this gap, this paper presents a novel unsupervised neural architecture called RegGeoNet to parameterize an unstructured point set into a completely regular image structure dubbed as deep geometry image (DeepGI), such that spatial coordinates of unordered points are recorded in three-channel grid pixels. Intuitively, our goal is to embed irregular 3D surface points onto uniform 2D lattice grids, while trying to preserve local neighborhood consistency. Functionally, DeepGI serves as a generic representation modality for raw point cloud data and can be conveniently integrated into mature image processing pipelines. Driven by its unique structural characteristics, we are motivated to customize a set of efficient feature extractors that directly operate on DeepGIs for achieving a rich variety of downstream tasks. To demonstrate the potential and universality of our proposed learning paradigms built upon DeepGIs for large-scale point cloud processing, we conduct extensive experiments on various downstream tasks, including shape classification, object part segmentation, scene semantic segmentation, normal estimation, and geometry compression, where our frameworks achieve highly competitive performance, compared with state-of-the-art methods. The source code will be publicly available at https://github.com/keeganhk/RegGeoNet.
Research Area(s)
- Deep learning, Large-scale 3D point clouds, Regular representation, Geometry image, Unsupervised learning
Citation Format(s)
RegGeoNet: Learning Regular Representations for Large-Scale 3D Point Clouds. / Zhang, Qijian; Hou, Junhui; Qian, Yue et al.
In: International Journal of Computer Vision, Vol. 130, No. 12, 12.2022, p. 3100–3122.
In: International Journal of Computer Vision, Vol. 130, No. 12, 12.2022, p. 3100–3122.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review