Global Point Cloud Registration Network for Large Transformations

• URL: http://arxiv.org/abs/2403.18040v1
• Date: Tue, 26 Mar 2024 18:52:48 GMT
• Title: Global Point Cloud Registration Network for Large Transformations
• Authors: Hanz Cuevas-Velasquez, Alejandro Galán-Cuenca, Antonio Javier Gallego, Marcelo Saval-Calvo, Robert B. Fisher,
• Abstract summary: We present ReLaTo, an architecture that faces the cases where large transformations happen while maintaining good performance for local transformations. This paper uses a novel Softmax pooling layer to find correspondences in a bilateral consensus manner between two point sets, sampling the most confident matches. A target-guided denoising step is then applied to both the obtained matches and latent features, estimating the final fine registration.
• Score: 46.7301374772952
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Three-dimensional data registration is an established yet challenging problem that is key in many different applications, such as mapping the environment for autonomous vehicles, and modeling objects and people for avatar creation, among many others. Registration refers to the process of mapping multiple data into the same coordinate system by means of matching correspondences and transformation estimation. Novel proposals exploit the benefits of deep learning architectures for this purpose, as they learn the best features for the data, providing better matches and hence results. However, the state of the art is usually focused on cases of relatively small transformations, although in certain applications and in a real and practical environment, large transformations are very common. In this paper, we present ReLaTo (Registration for Large Transformations), an architecture that faces the cases where large transformations happen while maintaining good performance for local transformations. This proposal uses a novel Softmax pooling layer to find correspondences in a bilateral consensus manner between two point sets, sampling the most confident matches. These matches are used to estimate a coarse and global registration using weighted Singular Value Decomposition (SVD). A target-guided denoising step is then applied to both the obtained matches and latent features, estimating the final fine registration considering the local geometry. All these steps are carried out following an end-to-end approach, which has been shown to improve 10 state-of-the-art registration methods in two datasets commonly used for this task (ModelNet40 and KITTI), especially in the case of large transformations.

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