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

• URL: http://arxiv.org/abs/2109.00113v1
• Date: Tue, 31 Aug 2021 23:27:33 GMT
• Title: CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds
• Authors: Eric-Tuan L\^e, Minhyuk Sung, Duygu Ceylan, Radomir Mech, Tamy Boubekeur and Niloy J. Mitra
• Abstract summary: 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. 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%.
• Score: 51.47100091540298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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%.

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