In the process of extracting tailings ponds from large scene remote sensing images, semantic segmentation models usually perform calculations on all small-size remote sensing images segmented by the sliding window method. However, some of these small...
Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and hi...
Large-scene space-borne SAR images with a high resolution are particularly effective for monitoring vast oceanic areas globally. However, ships are easily overlooked in such large scenes due to their small size and cluttered backgrounds, making SAR s...
Unmanned Aerial Vehicles (UAVs) are able to provide instantaneous visual cues and a high-level data throughput that could be further leveraged to address complex tasks, such as semantically rich scene understanding. In this work, we built on the use...
Face detection for security cameras monitoring large and crowded areas is very important for public safety. However, it is much more difficult than traditional face detection tasks. One reason is, in large areas like squares, stations and stadiums, f...
In this paper, a new Region-based Convolutional Neural Networks (RCNN) method is proposed for target recognition in large scene synthetic aperture radar (SAR) images. To locate and recognize the targets in SAR images, there are three steps in the tra...
Free of the constraints of orbit mechanisms, weather conditions and minimum antenna area, synthetic aperture radar (SAR) equipped on near-space platform is more suitable for sustained large-scene imaging compared with the spaceborne and airborne coun...
While 3D Gaussian Splatting (3DGS) has significantly advanced large-scale 3D reconstruction and novel view synthesis, it still suffers from high memory consumption and slow training speed. To address these issues without compromising reconstruction q...
The current method of crack detection in bridges using unmanned aerial vehicles (UAVs) relies heavily on acquiring local images of bridge concrete components, making image acquisition inefficient. To address this, we propose a crack detection method...
Large-scale scene rendering faces challenges in managing massive scene data and mitigating rendering latency caused by suboptimal loading sequences. Although current approaches utilize Level of Detail (LOD) for dynamic resource loading, two limitatio...
Developing Arithmetic Word Problem (AWP) -solving algorithms has recently become one of the hottest research areas because it can simultaneously advance general artificial intelligence and the application of AI technology in education. This paper pre...
As a widely used display device, the effective display area of a digital micromirror device (DMD) is limited by its micromirror count and pitch, which cannot meet large-format target-plane display requirements. This paper proposes a large-format scen...
This paper studies geometric scene formalization in vision-based educational sensing environments, where textual conditions and geometric diagram images jointly constitute heterogeneous perceptual inputs. The goal is to convert multimodal sensed info...
High-definition images covering entire large-scene construction sites are increasingly used for monitoring management. However, the transmission of high-definition images is a huge challenge for construction sites with harsh network conditions and sc...
Object detection from unmanned aerial vehicle (UAV) imagery is essential for applications such as traffic monitoring, disaster response, and urban surveillance, yet most existing methods are developed and evaluated under clear-sky conditions. In real...
Modeling large-scale scenarios of diversity in real forests is a hot topic in forestry research. At present, there is a common problem of simple and poor model scalability in large-scale forest scenes. Forest growth is often carried out using a holis...
The reconstruction of realistic large-scale 3D scene models using aerial images or videos has significant applications in smart cities, surveying and mapping, the military and other fields. In the current state-of-the-art 3D-reconstruction pipeline,...
This article primarily focuses on the study of image-based localization technology. While traditional methods have made significant advancements in technology and applications, the emerging field of visual image-based localization technology demonstr...
Scene reconstruction uses images or videos as input to reconstruct a 3D model of a real scene and has important applications in smart cities, surveying and mapping, military, and other fields. Structure from motion (SFM) is a key step in scene recons...
Efficient and realistic large-scale scene modeling is an important application of low-altitude remote sensing. Although the emerging 3DGS technology offers a simple process and realistic results, its high computational resource demands hinder direct...
The demand for location-based services (LBS) in large indoor spaces, such as airports, shopping malls, museums and libraries, has been increasing in recent years. However, there is still no fully applicable solution for indoor positioning and navigat...
Efficiently reconstructing complex and intricate surfaces at scale remains a significant challenge in 3D surface reconstruction. Recently, implicit neural representations have become a popular topic in 3D surface reconstruction. However, how to handl...
Car-like mobile robots (CLMRs) are extensively utilized in various intricate scenarios owing to their exceptional maneuverability, stability, and adaptability, in which path planning is an important technical basis for their autonomous navigation. Ho...
The end-operation accuracy of the satellite-borne robotic arm is closely related to the satellite attitude control accuracy, and the influence of the vibration of the satellite’s flexural structure on the satellite attitude control is not negli...
Diverse scene stitching is a challenging task in aerial video surveillance. This paper presents a hybrid stitching method based on the observation that aerial videos captured in real surveillance settings are neither totally ordered nor completely un...
The accurate identification of natural disasters is crucial in ensuring effective post-disaster relief efforts. However, the existing models for disaster classification often incur high costs. To address this, we propose leveraging the most advanced...
Reconstructing large-scale scenes using Neural Radiance Fields (NeRFs) is a research hotspot in 3D computer vision. Existing MLP (multi-layer perception)-based methods often suffer from issues of underfitting and a lack of fine details in rendering l...
Human head statistics is widely used in the construction of smart cities and has great market value. In order to solve the problem of missing pedestrian features and poor statistics results in a large-angle overlooking scene, in this paper we propose...
Using unmanned aerial vehicles (UAVs) for remote sensing has the advantages of high flexibility, convenient operation, low cost, and wide application range. It fills the need for rapid acquisition of high-resolution aerial images in modern photogramm...
Due to advantages such as its low power consumption and higher concealment, deceptive jamming against synthetic aperture radar (SAR) has received extensive attention during the past decades. However, large-scene deception jamming is still a challenge...
The emergence of Gaussian Splatting has ushered in a new phase in large-scale scene geometry reconstruction. Existing research in this field primarily focuses on computational efficiency and often neglects the effective utilization of inherent struct...
In this paper, we propose a fast Line Segment Detection algorithm for Polarimetric synthetic aperture radar (PolSAR) data (PLSD). We introduce the Constant False Alarm Rate (CFAR) edge detector to obtain the gradient map of the PolSAR image, which te...
Robust Arithmetic Word Problem (AWP) solving is important for applying mathematical reasoning systems in educational scenarios, where problem statements may contain changed numerical values, paraphrased descriptions, or irrelevant distracting informa...
Three-dimensional reconstruction is a key technology employed to represent virtual reality in the real world, which is valuable in computer vision. Large-scale 3D models have broad application prospects in the fields of smart cities, navigation, virt...
Text-driven 3D indoor scene synthesis has witnessed significant progress through Large Language Model (LLM)-based frameworks like ReSpace. However, current paradigms heavily rely on retrieving objects from pre-defined, static 3D asset libraries, whic...
Semantic segmentation of large-scale 3D point clouds is a fundamental task in robotic perception, semantic mapping, and urban scene understanding. Existing methods mainly rely on geometric information, which limits their ability to distinguish semant...
Natural-language-driven robot navigation in complex indoor environments requires the joint capability of high-fidelity scene representation, structured semantic reasoning, and executable path planning. To address this challenge, this paper proposes H...
The integration of thermal imaging data with multimodal large language models (MLLMs) offers promising advancements for enhancing the safety and functionality of autonomous driving systems (ADS) and intelligent transportation systems (ITS). This stud...
Panoptic scene graph generation (PSG) aims to simultaneously segment both foreground objects and background regions while predicting object relations for fine-grained scene modeling. Despite significant progress in panoptic scene understanding, curre...
In unmanned aerial vehicle (UAV) large-scale scene modeling, challenges such as missed shots, low overlap, and data gaps due to flight paths and environmental factors, such as variations in lighting, occlusion, and weak textures, often lead to incomp...
In response to the challenges of low computational efficiency, insufficient detail restoration, and dependence on multiple GPUs in 3D Gaussian Splatting for large-scale UAV scene reconstruction, this study introduces an improved 3D Gaussian Splatting...
Forest fire monitoring in remote sensing imagery has long relied on traditional perception models that primarily focus on detection or segmentation. However, such approaches fall short in understanding complex fire dynamics, including contextual reas...
3D scene graphs connect spatial perception with high-level language reasoning, but unverified false-positive groundings can impair downstream subtask generation. We propose a feed-forward, spatially constrained framework that grounds task-relevant ob...
Mass companion animal mortality scenes pose major veterinary forensic and animal welfare challenges, particularly when remains are commingled, variably decomposed, or skeletonized. In March 2023, the remains of 1256 dogs and cats were recovered from...
Unmanned Aerial Vehicle (UAV) orthophoto generation in complex environments remains challenging because weak textures, reflective surfaces, occlusions, and large scene extents can cause incomplete reconstruction, ghosting, and seam artifacts. Althoug...
While the wavenumber-domain approach enables large-angle inverse synthetic aperture radar (ISAR) cross-range scaling, its practical application remains constrained by the target’s non-uniform rotation and scene center offset (SCO). In response...
Analyzing events in dynamic environments poses a fundamental challenge in the development of intelligent agents and robots capable of interacting with humans. Current approaches predominantly rely on visual–text models; however, these methods o...
This paper presents the latent model-joint embedding predictive architecture (LM-JEPA), a resource-efficient collaborative perception framework for connected and autonomous vehicles that integrates latent predictive representation learning with light...
Noncontact, high-fidelity data acquisition has enabled terrestrial laser scanning (TLS) to be widely adopted for bridge geometry measurement and condition monitoring. In TLS applications, point cloud registration directly affects data quality and the...
With the rapid advancement of artificial intelligence and robotics, the integration of Large Language Models (LLMs) with 3D vision is emerging as a transformative approach to enhancing robotic sensing technologies. This convergence enables machines t...
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