Global Point Cloud Registration Network for Large Transformations
Hanz Cuevas-Velasquez Alejandro Galán-Cuenca Antonio Javier Gallego Marcelo Saval-Calvo Robert B. Fisher
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.