We present CAGE (Continuity-Aware edGE) network, a robust framework for reconstructing vector floorplans directly from point-cloud density maps. Traditional corner-based polygon representations are highly sensitive to noise and incomplete observations, often resulting in fragmented or implausible layouts. Recent line grouping methods leverage structural cues to improve robustness but still struggle to recover fine geometric details. To address these limitations, we propose a native edge-centric formulation, modeling each wall segment as a directed, geometrically continuous edge. This representation enables inference of coherent floorplan structures, ensuring watertight, topologically valid room boundaries while improving robustness and reducing artifacts. Towards this design, we develop a dual-query transformer decoder that integrates perturbed and latent queries within a denoising framework, which not only stabilizes optimization but also accelerates convergence. Extensive experiments on Structured3D and SceneCAD show that CAGE achieves state-of-the-art performance, with F1 scores of 99.1% (rooms), 91.7% (corners), and 89.3% (angles). The method also demonstrates strong cross-dataset generalization, underscoring the efficacy of our architectural innovations. Code and pretrained models are available on our project page: https://github.com/ee-Liu/CAGE.git.

This paper tackles the challenge of Clothes-Changing person Re-Identification (CC-ReID) through the dual lenses of model development and dataset creation. Unlike conventional person ReID, which presumes that individuals maintain consistent clothing, CC-ReID acknowledges the frequent changes in attire encountered in real-world scenarios. This task is particularly difficult due to the substantial variations in visual cues such as colors and textures that accompany changes in clothing. We propose a Multi-modal Assisted feature Learning Framework (MAL-F) designed to learn representations that are invariant to color and texture by utilizing RGB, grayscale, and contour images. MAL-F is a versatile framework that can be seamlessly integrated with existing CC-ReID models, significantly enhancing their accuracy. To further reduce the impact of clothing variations, we introduce a novel CC-ReID backbone named ResTNet, which combines ResNet with a Transformer. ResTNet features a Non-Clothing Salient Region (NC-SR) reinforcement transformer module that employs random clothing block drop and spatial activation mapping. These methods direct the model to emphasize key regions unrelated to clothing, enhancing these features with minimal computational overhead. This strategy substantially enhances CC-ReID performance and supports real-time video processing. Furthermore, recognizing the limitations of current small-scale CC-ReID datasets, we have developed two larger datasets: FBCC and Market1501-CC. Extensive experimental results demonstrate that our proposed methodology surpasses existing state-of-the-art methods for CC-ReID in terms of accuracy, robustness, and efficiency.

Detecting land use changes in urban areas from very-high-resolution (VHR) satellite images presents two primary challenges: 1) traditional methods focus mainly on comparing changes in land cover-related features, which are insufficient for detecting changes in land use and are prone to pseudo-changes caused by illumination differences, seasonal variations, and subtle structural changes and 2) spatial structural information, which is characterized by topological relationships among land cover objects, is crucial for urban land use classification but remains underexplored in change detection. To address these challenges, this study developed a local-global structural interaction network (LGSI-Net) based on a Siamese graph neural network (SGNN) that integrates high-level structural and semantic information to detect urban land use changes from bitemporal VHR images. We developed both local structural feature interaction module (LSIM) and global structural feature interaction module (GSIM) to enhance the representation of bitemporal structural features at the global scene graph and local object node levels. Experiments on the publicly available MtS-WH dataset and two generated datasets, LUCD-FZ and LUCD-HF, show that the proposed method outperforms the existing bag of visual word (BoVW)-based method and CorrFusionNet. Furthermore, we evaluated the detection performance for different semantic feature extraction strategies and structural feature extraction backbones. The results demonstrate that the proposed method, which integrates high-level semantic and graph isomorphism network (GIN)-derived structural features achieves the best performance. The method trained on the LUCD-FZ dataset was successfully transferred to the LUCD-HF dataset with different urban landscapes, indicating its effectiveness in detecting land use changes from VHR satellite images, even in areas with relatively large imbalances between changed and unchanged samples.

Waveform decomposition is needed as a first step in the extraction of various types of geometric and spectral information from hyperspectral full-waveform LiDAR echoes. We present a new approach to deal with the ”Pseudo-monopulse” waveform formed by the overlapped waveforms from multi-targets when they are very close. We use one single skew-normal distribution (SND) model to fit waveforms of all spectral channels first and count the geometric center position distribution of the echoes to decide whether it contains multi-targets. The geometric center position distribution of the ”Pseudo-monopulse” presents aggregation and asymmetry with the change of wavelength, while such an asymmetric phenomenon cannot be found from the echoes of the single target. Both theoretical and experimental data verify the point. Based on such observation, we further propose a hyperspectral waveform decomposition method utilizing the SND mixture model with: 1) initializing new waveform component parameters and their ranges based on the distinction of the three characteristics (geometric center position, pulse width, and skew-coefficient) between the echo and fitted SND waveform and 2) conducting single-channel waveform decomposition for all channels and 3) setting thresholds to find outlier channels based on statistical parameters of all single-channel decomposition results (the standard deviation and the means of geometric center position) and 4) re-conducting single-channel waveform decomposition for these outlier channels. The proposed method significantly improves the range resolution from 60cm to 5cm at most for a 4ns width laser pulse and represents the state-of-the-art in ”Pseudo-monopulse” waveform decomposition.

In this paper, we present an improved framework for the instance-aware semantic segmentation of road furniture in mobile laser scanning data. In our framework, we first detect road furniture from mobile laser scanning point clouds. Then we decompose the detected pieces of road furniture into poles and their attached components, and extract the instance information of the components with different features. Most importantly, we classify the components into different categories by combining a classifier and a probabilistic graphic model named DenseCRF, which is the major contribution of this paper. For the classification of the components using DenseCRF, the unary potentials and the pairwise potentials are first obtained. The unary potentials are obtained from the classifier which takes the instance information of components as the input. The pairwise potentials are calculated considering contextual relations between components. By utilising DenseCRF, the contextual consistency of components is preserved, and the performance is significantly improved compared to our previous work. We collect three datasets to test our framework, and compare the classification performances of six different classifiers with and without DenseCRF. The combination of random forest with DenseCRF outperforms the other methods and achieves high overall accuracies of 83.7%, 96.4% and 95.3% in these three datasets. Experimental results demonstrate that our framework reliably assigns both semantic information and instance information for mobile laser scanning point clouds of road furniture.

This study utilizes the unmanned aerial vehicle (UAV) to acquire high resolution images for feature matching, resulting in a point cloud. A progressive morphological filter is used to filter out nonground object points from point cloud. Multi-scale and different shape filter windows are adopted for the morphological filter to achieve good performance. The results show that multi-scale and multi-shape window can improve the performance of morphological filter compared with single direction filter window, as nonground objects cannot be completely removed with single direction filter window. With multi-shape or 2-D filter window, buildings can be effectively removed and ground points can be reserved.

There is no single sensor can acquire the complete information for disaster monitoring. This study investigates the applicability of registering multiple point clouds obtained from unmanned aerial vehicle (UAV) images and terrestrial laser scanning (TLS). Low attitude images with high overlaps were collected by an eight-rotor UAV platform and image-based 3D modeling techniques are used to generate 3D point cloud, covering most of roof information of the damaged buildings. TLS was used to collect the side information of the damaged buildings with multiple scans. Point clouds from the two platforms are iteratively registered using a method, from coarse to fine, to get complete geometry of the study area. Geometric features are subsequently extracted to help for the identification of damage degree of buildings. Experimental result shows that by analyzing the intersection lines of plane features, we can further detect the building's inclination.

We address the problem of efficient image matching for large, highly redundant photo collections with highly complex topologies, such as photos acquired by unmanned aerial vehicles (UAV), focusing on disaster monitoring. Our approach conducts a skeleton graph which simplifies the image topology with the consideration of image importance and topological relationship. We define the image with the highest importance weight as candidate, adding the remaining images referring to the topology skeleton. To conduct the skeletal graph, the image topology is first computed depending on the overlapping relationships between images. Experimental results show that our technique drastically limits the searching range that is for feature similarity computation, resulting in dramatic speed up. A final bundler adjustment is implemented in the procedure of scene reconstruction, and the completeness and accuracy are far more comparable to the traditional method.