Feature Description with Feature Point Registration Error Using Local and Global Point Cloud Encoders

Kenshiro TAMATA  Tomohiro MASHITA  

IEICE TRANSACTIONS on Information and Systems   Vol.E105-D    No.1    pp.134-140
Publication Date: 2022/01/01
Publicized: 2021/10/11
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2021EDP7082
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
3D environment reconstruction,  point cloud,  registration,  machine learning,  

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A typical approach to reconstructing a 3D environment model is scanning the environment with a depth sensor and fitting the accumulated point cloud to 3D models. In this kind of scenario, a general 3D environment reconstruction application assumes temporally continuous scanning. However in some practical uses, this assumption is unacceptable. Thus, a point cloud matching method for stitching several non-continuous 3D scans is required. Point cloud matching often includes errors in the feature point detection because a point cloud is basically a sparse sampling of the real environment, and it may include quantization errors that cannot be ignored. Moreover, depth sensors tend to have errors due to the reflective properties of the observed surface. We therefore make the assumption that feature point pairs between two point clouds will include errors. In this work, we propose a feature description method robust to the feature point registration error described above. To achieve this goal, we designed a deep learning based feature description model that consists of a local feature description around the feature points and a global feature description of the entire point cloud. To obtain a feature description robust to feature point registration error, we input feature point pairs with errors and train the models with metric learning. Experimental results show that our feature description model can correctly estimate whether the feature point pair is close enough to be considered a match or not even when the feature point registration errors are large, and our model can estimate with higher accuracy in comparison to methods such as FPFH or 3DMatch. In addition, we conducted experiments for combinations of input point clouds, including local or global point clouds, both types of point cloud, and encoders.

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