Deep Global Features for Point Cloud Alignment

Sensors
Ahmed El KhazariHyo Jong Lee

Abstract

Point cloud registration is a key problem in computer vision applications and involves finding a rigid transform from a point cloud into another such that they align together. The iterative closest point (ICP) method is a simple and effective solution that converges to a local optimum. However, despite the fact that point cloud registration or alignment is addressed in learning-based methods, such as PointNetLK, they do not offer good generalizability for point clouds. In this stud, we proposed a learning-based approach that addressed existing problems, such as finding local optima for ICP and achieving minimum generalizability. The proposed model consisted of three main parts: an encoding network, an auxiliary module that weighed the contribution of each input point cloud, and feature alignment to achieve the final transform. The proposed architecture offered greater generalization among the categories. Experiments were performed on ModelNet40 with different configurations and the results indicated that the proposed approach significantly outperformed the state-of-the-art point cloud alignment methods.

References

Sep 14, 2006·IEEE Transactions on Medical Imaging·Arkadiusz SitekGrant T Gullberg
May 11, 2013·IEEE Transactions on Visualization and Computer Graphics·Gary K L TamPaul L Rosin
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence· Yulan Guo Jianwei Wan

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Methods Mentioned

BETA
features extraction

Software Mentioned

Awe
Go
AweNet
Net
PointNetLK
LUT
ICP
EMD
PointNet
ICDO

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