3D Gaussian splatting representations have demonstrated outstanding potential for dense scene reconstruction and simultaneous localization and mapping (SLAM). However, existing multi-view filtering techniques frequently overlook critical edge information, resulting in blurred reconstruction boundaries. To address these challenges, we propose High-fidelity Gaussian SLAM based on Optical Flow Assisted Tracking (HGS_OFAT). Our approach analyzes the relationships between edge cues and pixels selected by multi-view pixel selection strategy, and employs a prudent initialization strategy to optimize Gaussian parameters—thereby reducing redundancy and improving reconstruction fidelity. Furthermore, HGS_OFAT integrates an opticalflow tracker before Gaussian-based reconstruction to obtain accurate camera poses, significantly enhancing both localization and reconstruction accuracy. Experiments on synthetic and realworld datasets demonstrate that HGS_OFAT outperforms existing methods in tracking robustness and reconstruction quality.

Point cloud registration remains a fundamental yet challenging problem in computer vision, particularly under low-overlap conditions. We propose a Multi-Geometric Fusion Transformer (MGFT) that leverages rich geometric cues to enhance registration accuracy. MGFT integrates geometric features extracted by a Graph Convolutional Network (GCN) and Point Pair Features (PPF) into a Transformer-based framework for robust feature representation and interaction. Additionally, an overlap prediction module estimates the overlap score between the two point clouds, improving resilience in challenging scenarios. Extensive experiments on both indoor and outdoor benchmarks demonstrate that MGFT achieves state-of-the-art performance in point cloud registration tasks, particularly in low-overlap scenarios.

As one of the most destructive natural disasters, earthquakes have struck many countries around the world in recent years, causing serious economic losses. Change detection (CD) can be applied to postearthquake building CD as it can infer interested change regions from multitemporal remote sensing (RS) images. Furthermore, the CD with short imaging intervals will better satisfy the needs of the emergency rescues after earthquakes. However, the capability of current methods built on deep neural networks (DNNs) is limited because the dataset with short imaging intervals is absent. To meet postdisaster immediate relief, we create a CD dataset, the Turkey earthquake CD dataset (TUE-CD), for the detection of building collapse in the short term after an earthquake. Due to the high requirement for timeliness of postevent images, the orbit of the satellite during postevent imaging deviates from that during preevent imaging, which leads to a side-looking problem between bitemporal images. To deal with these challenges, we present a multiscale feature interaction network (MSI-Net) for efficient interaction between bitemporal features, as well as mitigating the effect of side-looking problems. Specifically, the proposed MSI-Net consists of joint cross-attention (JCA) modules, multiscale offset calibration (MOC) modules, and feature integration (FeI) modules. The JCA module unifies channel cross-attention (CCA) and spatial joint attention (SJA) for sufficient feature interaction. The MOC module further estimates the offsets to align the bitemporal image with the multiscale features. Finally, calibrated features and multiscale features are fused by FeI modules for the prediction of changed areas. The best mF1 and mIoU scores are achieved on two public datasets and the constructed TUE-CD dataset: WHU-CD (95.58%, 91.81%), CLCD (82.96%, 73.53%), and TUE-CD (78.02%, 68.48%). Experimental results demonstrate that the proposed MSI-Net provides competitive performance compared to the state-of-the-art CD methods. The TUE-CD dataset and the code of MSI-Net will be available athttps://github.com/RSMagneto/MSI-Net.

The integration of spatial and spectral information is beneficial to the improvement of change detection (CD) performance. However, existing methods cannot efficiently suppress the influences of spatial and spectral differences (SDs) in unchanged areas. To address these issues, in this article, we propose a content-guided spatial–spectral integration network (CSI-Net) for the fusion of global spatial details and SD information. Specifically, the proposed CSI-Net is composed of a spatial reasoning (SR) module, an SD module, and a content-guided integration (CGI) module. In the SR module, the spatial information is learned by cascaded graph convolution (GC) blocks for global modeling. The SD module is responsible for the extraction of spectral features, by calculating the means and variances of features to reduce the impact of SDs in unchanged regions. In addition, in order to integrate the spatial–spectral features efficiently, we design a CGI module to further take advantage of their complementary information. In this module, high-level content information is introduced as a guide for proper interaction. Due to the efficient spatial–spectral fusion, the proposed CSI-Net can learn the changed features better while achieving suppression of SDs. Experimental results on LEVIR-CD, WHU-CD, and CLCD datasets demonstrate that the proposed CSI-Net produces better performance compared to state-of-the-art methods, and is applicable to different scenarios. The code of CSI-Net is available athttps://github.com/RSMagneto/CSI-Net.

Image-to-Color point cloud registration establishes a connection between two-dimensional image data and three-dimensional point cloud data, and plays a vital role in the intelligent city, autonomous driving, and robotics field. However, it is still challenging to automatically register an image and its surrounding color point clouds together, due to the special application scenario, unknown camera intrinsic parameters, lack of train data, and a large amount of noise. To overcome those issues, we propose an iterative reprojection architecture that automatically acquires the matched 2D-3D keypoints pairs between the image and the color point clouds by graph optimization method and mapping transfer first, then completes registration by Alternating Direction Method of Multipliers (ADMM). Experiments results show that the proposed method is more accurate than the manual way.

Abstract Mobile edge computing has been developed as a promising technology to extend diverse services to the edge of the Internet of Things system. Motivated by the high flexibility and controllability of unmanned aerial vehicles (UAVs), a multi‐UAVs‐assisted mobile edge computing system is studied to reduce the total consumption of time and energy of terminal equipments. In this system, UAVs act as the computing nodes or relay nodes for process terminal equipment's task. Accordingly, an optimisation problem is formulated to minimise the weighted sum of energy and delay consumption in the edge computing network. To solve the problem, an asynchronous advantage actor–critic (A3C) based deep reinforcement learning algorithm is proposed to obtain the optimal strategy for computation offloading and resource allocation. Experimental results demonstrate that the proposed A3C based algorithm converges fast and outperforms the baseline algorithms in terms of the energy and time consumption of system.

In recent years, 3D target detection and location methods in the field of autonomous driving have attracted increasing attention, but monocular 3D pedestrian localization research is still facing challenges. In this paper, a monocular pedestrian localization framework and a fine-grained location optimization method which is based on a bird's eye view are proposed. The monocular pedestrian localization framework is divided into three parts, which are coarse-grained localization, depth information reconstruction and fine-grained location optimization. In the stage of coarse-grained location, the human skeleton information is obtained from the original image by using the human skeleton point detection method, and then the pedestrian position is predicted through the method of a light-weight feed-forward neural network. In the stage of depth information reconstruction, the original image is used to reconstruct the corresponding bird's eye view with depth information through a parallel network. Finally, a fine-grained positioning optimization method makes it possible to get a more precise location with the help of the last two stages. The experimental results on the KITTI dataset show that our method has achieved better performance than the state-of-the-art methods.

With rapid development of light detection and ranging (LiDAR) technologies, three dimensional point clouds increasingly become a new approach to sense the world. In our previous work, light poles were detected from mobile LiDAR point clouds without using their locations. In this paper, we improve our previous work by considering location information between two neighboring light poles to reduce false alarm. In the proposed method, the potential light poles are first detected by the extended Hough Forest Framework. Then, a gaussian distribution is exploited to model the distance between two light poles by using locations of those detected light poles. Finally, inaccurately detected light poles are removed by considering the distance between two adjacent objects. We evaluate our proposed method on mobile LiDAR point clouds acquired by RIEGL VMX-450 system. On the basis of the experimental test instances, we demonstrate improved accuracy on light pole detection.