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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