CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds

Eric-Tuan Le, Minhyuk Sung, Duygu Ceylan, Radomir Mech, Tamy Boubekeur, Niloy J. Mitra; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 7457-7466

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++, 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

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Le_2021_ICCV, author = {Le, Eric-Tuan and Sung, Minhyuk and Ceylan, Duygu and Mech, Radomir and Boubekeur, Tamy and Mitra, Niloy J.}, title = {CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {7457-7466} }