Abstract Point cloud registration is a crucial aspect of computer vision and 3D reconstruction. Traditional registration methods often depend on global features or iterative optimisation, leading to inefficiencies and imprecise outcomes when processing complex scene point cloud data. To address these challenges, the authors introduce a principal direction‐guided local voxelisation structural feature (PDLVSF) approach for point cloud registration. This method reliably identifies feature points regardless of initial positioning. Approach begins with the 3D Harris algorithm to extract feature points, followed by determining the principal direction within the feature points' radius neighbourhood to ensure rotational invariance. For scale invariance, voxel grid normalisation is utilised to maximise the point cloud's geometric resolution and make it scale‐independent. Cosine similarity is then employed for effective feature matching, identifying corresponding feature point pairs and determining transformation parameters between point clouds. Experimental validations on various datasets, including the real terrain dataset, demonstrate the effectiveness of our method. Results indicate superior performance in root mean square error (RMSE) and registration accuracy compared to state‐of‐the‐art methods, particularly in scenarios with high noise, limited overlap, and significant initial pose rotation. The real terrain dataset is publicly available at https://github.com/black‐2000/Real‐terrain‐data .

On-orbit geometric calibration is crucial for generating high-quality satellite products. Existing methods are often costly or complex, requiring manual control points or specialized orbit relations. This paper introduces a novel approach using accurate 3D contours from city-level aerial LiDAR point clouds. Our method, leveraging open-source LiDAR data, is both free and highly efficient. We employ a distance-transformation-based registration model and an iterative robust solver for patch-level image-point-cloud registration and global optimization. During the registration process, an a-contrario judgment model helps detect and filter out false results. Experiments with Santa Clara and Las Vegas data, using quality-level-1 LiDAR point clouds from the 3D Elevation Program of the US Geological Survey, showed that sub-meter resolution satellite images acquired by WorldView-2, GeoEye-2, and the newly launched Wuhan-1 satellite can achieve subpixel accuracy via the proposed automatic on-orbit calibration approach without any manual operation. Applied to future satellite clusters, the proposed approach has great potential for fast on-orbit production of remote sensing products and significantly reducing the maintenance costs of optical satellites.

The fusion of infrared and visible images aims to generate a composite image that can simultaneously contain the thermal radiation information of an infrared image and the plentiful texture details of a visible image to detect targets under various weather conditions with a high spatial resolution of scenes. Previous deep fusion models were generally based on convolutional operations, resulting in a limited ability to represent long-range context information. In this paper, we propose a novel end-to-end model for infrared and visible image fusion via a dual attention Transformer termed DATFuse. To accurately examine the significant areas of the source images, a dual attention residual module (DARM) is designed for important feature extraction. To further model long-range dependencies, a Transformer module (TRM) is devised for global complementary information preservation. Moreover, a loss function that consists of three terms, namely, pixel loss, gradient loss, and structural loss, is designed to train the proposed model in an unsupervised manner. This can avoid manually designing complicated activity-level measurement and fusion strategies in traditional image fusion methods. Extensive experiments on public datasets reveal that our DATFuse outperforms other representative state-of-the-art approaches in both qualitative and quantitative assessments. The proposed model is also extended to address other infrared and visible image fusion tasks without fine-tuning, and the promising results demonstrate that it has good generalization ability. The source code is available athttps://github.com/tthinking/DATFuse.

Unsupervised person re-identification (Re-ID) aims to learn discriminative features without human-annotated labels. Recently, contrastive learning has provided a new prospect for unsupervised person Re-ID, and existing methods primarily constrain the feature similarity among easy sample pairs. However, the feature similarity among hard sample pairs is neglected, which yields suboptimal performance in unsupervised person Re-ID. In this paper, we propose a novel Hybrid Contrastive Model (HCM) to perform the identity-level contrastive learning and the image-level contrastive learning for unsupervised person Re-ID, which adequately explores feature similarities among hard sample pairs. Specifically, for the identity-level contrastive learning, an identity-based memory is constructed to store pedestrian features. Accordingly, we define the dynamic contrast loss to identify identity information with dynamic factor for distinguishing hard/easy samples. As for the image-level contrastive learning, an image-based memory is established to store each image feature. We design the sample constraint loss to explore the similarity relationship between hard positive and negative sample pairs. Furthermore, we optimize the two contrastive learning processes in one unified framework to make use of their own advantages as so to constrain the feature distribution for extracting potential information. Extensive experiments demonstrate that the proposed HCM distinctly outperforms existing methods.

The 3D point cloud is a common 3D data representation that has received increasing attention for remote sensing applications. However, processing 3D point cloud semantics, especially local semantic information, has always been a challenge and has attracted much attention. In this paper, we propose a novel enhanced local semantic learning transformer for 3D point cloud analysis, which aims to enhance the transformer awareness of local semantic features to handle complex point cloud tasks. First, we propose a novel transformer framework, the local semantic learning point cloud transformer (LSLPCT), which not only learns 3D point clouds the global information, but also enhances the perception of local semantic information end-to-end. Second, we design an efficient local semantic learning self-attention mechanism, namely LSL-SA, which can parallelize the perception of global contextual information and the capture of finer-grained local semantic features. Third, our proposed LSL-SA is easy to implement and can integrate existing transformers and CNN-based networks for processing various point cloud tasks. Numerous experiments in different types of point cloud tasks have been conducted, and our method performs better or is competitive with other state-of-the-art methods.

Discriminating leaf and wood components in terrestrial laser scanning (TLS) point clouds is a prerequisite for accurately estimating 3-D structural and biophysical attributes of both individual trees and entire forests. However, most existing separation methods are conducted at local (i.e., individual or plot) level. The local level separation methods need a presegmentation of the acquired point clouds, and the separation accuracy and reliability are greatly influenced by forest occlusion effect and point cloud qualities. A new generalized method merely based on differences in geometric features, including curvature, density, and salient features, is proposed in this study for separating leaf and wood components at the TLS single-scan level. A preliminary separation is conducted using the quantity of normal change rate (i.e., surface variation) given that leaf points often demonstrate sharp local curvature changes. Then, separation is continually conducted on the basis of calibrated density data (i.e., number of points in a given radius) because of the scattered orientations and small sizes of leaves. Finally, a new self-adjusting connectivity segmentation algorithm is proposed to group remaining points into different clusters. Leaf and wood clusters are separated in accordance with salient features and sizes simultaneously. Results indicate that derived geometric quantities from curvature, density, and salient features of individual points and segmented clusters can be jointly used to discriminate leaf and wood components effectively and robustly in single-scan TLS point clouds with a mean overall accuracy of approximately 93%. In addition, results show good performance in terms of the insensitivity to distance, instrument type, occlusion effect, and forest composition of the proposed method.

Owing to the limitations of imaging sensors, it is challenging to obtain a medical image that simultaneously contains functional metabolic information and structural tissue details. Multimodal medical image fusion, an effective way to merge the complementary information in different modalities, has become a significant technique to facilitate clinical diagnosis and surgical navigation. With powerful feature representation ability, deep learning (DL)-based methods have improved such fusion results but still have not achieved satisfactory performance. Specifically, existing DL-based methods generally depend on convolutional operations, which can well extract local patterns but have limited capability in preserving global context information. To compensate for this defect and achieve accurate fusion, we propose a novel unsupervised method to fuse multimodal medical images via a multiscale adaptive Transformer termed MATR. In the proposed method, instead of directly employing vanilla convolution, we introduce an adaptive convolution for adaptively modulating the convolutional kernel based on the global complementary context. To further model long-range dependencies, an adaptive Transformer is employed to enhance the global semantic extraction capability. Our network architecture is designed in a multiscale fashion so that useful multimodal information can be adequately acquired from the perspective of different scales. Moreover, an objective function composed of a structural loss and a region mutual information loss is devised to construct constraints for information preservation at both the structural-level and the feature-level. Extensive experiments on a mainstream database demonstrate that the proposed method outperforms other representative and state-of-the-art methods in terms of both visual quality and quantitative evaluation. We also extend the proposed method to address other biomedical image fusion issues, and the pleasing fusion results illustrate that MATR has good generalization capability. The code of the proposed method is available at https://github.com/tthinking/MATR.

In order to solve the accuracy problem caused by lens distortions of nonmetric digital cameras mounted on an unmanned aerial vehicle, the estimation for initial values of lens distortion must be studied. Based on the fact that radial lens distortions are the most significant of lens distortions, a simple and efficient method for radial lens distortion estimation is proposed in this paper. Starting from the coplanar equation, the geometric characteristics of the relative orientation equations are explored. This paper further proves that the radial lens distortion can be linearly estimated in a continuous relative orientation model. The proposed procedure only requires a sufficient number of point correspondences between two or more images obtained by the same camera; thus it is suitable for a natural scene where the lack of straight lines and calibration objects precludes most previous techniques. Both computer simulation and real data have been used to test the proposed method; the experimental results show that the proposed method is easy to use and flexible.