Le, Eric-Tuan;
Sung, Minhyuk;
Ceylan, Duygu;
Mech, Radomir;
Boubekeur, Tamy;
Mitra, Niloy J;
(2022)
CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds.
In:
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021).
(pp. pp. 7437-7446).
IEEE: Montreal, QC, Canada.
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Abstract
Representing human-made objects as a collection of base primitives has a long history in computer vision and reverse engineering. In the case of high-resolution point cloud scans, the challenge is to be able to detect both large primitives as well as those explaining the detailed parts. While the classical RANSAC approach requires case-specific parameter tuning, state-of-the-art networks are limited by memory consumption of their backbone modules such as PointNet++ [27], and hence fail to detect the fine-scale primitives. We present Cascaded Primitive Fitting Networks (CPFN) that relies on an adaptive patch sampling network to assemble detection results of global and local primitive detection networks. As a key enabler, we present a merging formulation that dynamically aggregates the primitives across global and local scales. Our evaluation demonstrates that CPFN improves the state-of-the-art SPFN performance by 13 - 14% on high-resolution point cloud datasets and specifically improves the detection of fine-scale primitives by 20 - 22%. Our code is available at: https://github.com/erictuanle/CPFN.
| Type: | Proceedings paper |
|---|---|
| Title: | CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds |
| Event: | 18th IEEE/CVF International Conference on Computer Vision (ICCV) |
| Location: | ELECTR NETWORK |
| Dates: | 11 Oct 2021 - 17 Oct 2021 |
| ISBN-13: | 9781665428125 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICCV48922.2021.00736 |
| Publisher version: | https://doi.org/10.1109/ICCV48922.2021.00736 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Theory & Methods, Computer Science |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159080 |
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