EGST: Enhanced Geometric Structure Transformer for Point Cloud Registration.

We explore the effect of geometric structure descriptors on extracting reliable correspondences and obtaining accurate registration for point cloud registration. The point cloud registration task involves the estimation of rigid transformation motion in unorganized point cloud, hence it is crucial to capture the contextual features of the geometric structure in point cloud. Recent coordinates-only methods ignore numerous geometric information in the point cloud which weaken ability to express the global context. We propose Enhanced Geometric Structure Transformer to learn enhanced contextual features of the geometric structure in point cloud and model the structure consistency between point clouds for extracting reliable correspondences, which encodes three explicit enhanced geometric structures and provides significant cues for point cloud registration. More importantly, we report empirical results that Enhanced Geometric Structure Transformer can learn meaningful geometric structure features using none of the following: (i) explicit positional embeddings, (ii) additional feature exchange module such as cross-attention, which can simplify network structure compared with plain Transformer. Extensive experiments on the synthetic dataset and real-world datasets illustrate that our method can achieve competitive results.

Explore the Influence of Shallow Information on Point Cloud Registration.

Feature extraction is a key step for deep-learning-based point cloud registration. In the correspondence-free point cloud registration task, the previous work commonly aggregates deep information for global feature extraction and numerous shallow information which is positive to point cloud registration will be ignored with the deepening of the neural network. Shallow information tends to represent the structural information of the point cloud, while deep information tends to represent the semantic information of the point cloud. In addition, fusing information of different dimensions is conducive to making full use of shallow information. Inspired by this, we verify shallow information in the middle layers can bring a positive impact on the point cloud registration task. We design various architectures to combine shallow information and deep information to extract global features for point cloud registration. Experimental results on the ModelNet40 dataset illustrate that feature extractors that incorporate shallow information will bring positive performance.

Commonality Autoencoder: Learning Common Features for Change Detection From Heterogeneous Images.

Change detection based on heterogeneous images, such as optical images and synthetic aperture radar images, is a challenging problem because of their huge appearance differences. To combat this problem, we propose an unsupervised change detection method that contains only a convolutional autoencoder (CAE) for feature extraction and the commonality autoencoder for commonalities exploration. The CAE can eliminate a large part of redundancies in two heterogeneous images and obtain more consistent feature representations. The proposed commonality autoencoder has the ability to discover common features of ground objects between two heterogeneous images by transforming one heterogeneous image representation into another. The unchanged regions with the same ground objects share much more common features than the changed regions. Therefore, the number of common features can indicate changed regions and unchanged regions, and then a difference map can be calculated. At last, the change detection result is generated by applying a segmentation algorithm to the difference map. In our method, the network parameters of the commonality autoencoder are learned by the relevance of unchanged regions instead of the labels. Our experimental results on five real data sets demonstrate the promising performance of the proposed framework compared with several existing approaches.