Partial Procedural Geometric Model Fitting for Point Clouds

10/17/2016
by   Zongliang Zhang, et al.
0

Geometric model fitting is a fundamental task in computer graphics and computer vision. However, most geometric model fitting methods are unable to fit an arbitrary geometric model (e.g. a surface with holes) to incomplete data, due to that the similarity metrics used in these methods are unable to measure the rigid partial similarity between arbitrary models. This paper hence proposes a novel rigid geometric similarity metric, which is able to measure both the full similarity and the partial similarity between arbitrary geometric models. The proposed metric enables us to perform partial procedural geometric model fitting (PPGMF). The task of PPGMF is to search a procedural geometric model space for the model rigidly similar to a query of non-complete point set. Models in the procedural model space are generated according to a set of parametric modeling rules. A typical query is a point cloud. PPGMF is very useful as it can be used to fit arbitrary geometric models to non-complete (incomplete, over-complete or hybrid-complete) point cloud data. For example, most laser scanning data is non-complete due to occlusion. Our PPGMF method uses Markov chain Monte Carlo technique to optimize the proposed similarity metric over the model space. To accelerate the optimization process, the method also employs a novel coarse-to-fine model dividing strategy to reject dissimilar models in advance. Our method has been demonstrated on a variety of geometric models and non-complete data. Experimental results show that the PPGMF method based on the proposed metric is able to fit non-complete data, while the method based on other metrics is unable. It is also shown that our method can be accelerated by several times via early rejection.

READ FULL TEXT

page 1

page 6

page 8

page 9

research
03/01/2021

OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration

Point cloud registration is a key task in many computational fields. Pre...
research
08/19/2021

PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers

Point clouds captured in real-world applications are often incomplete du...
research
04/18/2023

Variational Relational Point Completion Network for Robust 3D Classification

Real-scanned point clouds are often incomplete due to viewpoint, occlusi...
research
09/08/2020

Intraoperative Liver Surface Completion with Graph Convolutional VAE

In this work we propose a method based on geometric deep learning to pre...
research
09/22/2018

Geometric Multi-Model Fitting by Deep Reinforcement Learning

This paper deals with the geometric multi-model fitting from noisy, unst...
research
07/28/2017

Continuous Global Optimization in Surface Reconstruction from an Oriented Point Cloud

We introduce a continuous global optimization method to the field of sur...
research
09/16/2018

Testing SensoGraph, a geometric approach for fast sensory evaluation

This paper introduces SensoGraph, a novel approach for fast sensory eval...

Please sign up or login with your details

Forgot password? Click here to reset