Direct point-cloud visualisation is a common approach for visualising large datasets of aerial terrain LiDAR scans. However, because of the limitations of the acquisition technique, such visualisations often lack the desired visual appeal and quality...
Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual...
Computer-based data processing capabilities have evolved to handle a lot of information. As such, the complexity of three-dimensional (3D) models (e.g., animations or real-time voxels) containing large volumes of information has increased exponential...
Current software solutions for solar-radiation modeling in 3D focus on the urban environment. Most of the published tools do not implement methods to consider complex objects, such as urban greenery in their models or they expect a rather complex 3D...
Rock-mass point-cloud registration is a critical yet challenging task in the fields of geology and engineering. Currently, the lack of dedicated datasets for rock-mass point-cloud registration significantly limits the development and application of a...
Point-cloud is a valuable piece of information for geometric modeling and additive manufacturing of different types of objects. In most cases, a point-cloud is obtained by using the 3D scanners or by using image processing. Alternatively, one can rel...
To address the problem of point-cloud registration accuracy degradation or even failure in traditional Voxelized GICP(VGICP) under bad initial pose due to improper voxel resolution settings, this paper proposes an Adaptive Resolution VGICP (AR-VGICP)...
The expansion of tunnel scale has led to a massive demand for inspections. Light Detection And Ranging (LiDAR) has been widely applied in tunnel structure inspections due to its fast data acquisition speed and strong environmental adaptability. Howev...
The safety retaining wall is a critical infrastructure in ensuring the safety of both rock removal vehicles and personnel. However, factors such as precipitation infiltration, tire impact from rock removal vehicles, and rolling rocks can cause local...
Modern robotic exploratory strategies assume multi-agent cooperation that raises a need for an effective exchange of acquired scans of the environment with the absence of a reliable global positioning system. In such situations, agents compare the sc...
Acquiring high-fidelity 3D models from real-world scans is challenging. Existing shape completion-methods are incapable of generating details of objects or learning complex point distributions. To address this problem, we propose two transformer-base...
Neuromorphic Vision Sensors (NVSs) are emerging sensors that acquire visual information asynchronously when changes occur in the scene. Their advantages versus synchronous capturing (frame-based video) include a low power consumption, a high dynamic...
The creation of 2D–3D architectural vector drawings constitutes a manual, labor-intensive process. The scientific community has not provided an automated approach for the production of 2D–3D architectural drawings of cultural-heritage obj...
It has become routine to directly process point clouds using a combination of shared multilayer perceptrons and aggregate functions. However, this practice has difficulty capturing the local information of point clouds, leading to information loss. N...
Rutting is a critical form of pavement distress that compromises driving safety and long-term structural integrity. Traditional detection methods predominantly rely on cross-sectional measurements and high-cost inertial navigation-assisted laser scan...
Script-based computer-aided design tools offer accessible and customizable environments, but their broader adoption is limited by the cognitive and computational difficulty of describing curved, irregular, or free-form geometries through code. This s...
Incident solar radiation (insolation) passing through the forest canopy to the ground surface is either absorbed or scattered. This phenomenon, known as radiation attenuation, is measured using the extinction coefficient (K). The amount of radiation...
The phenotyping of plant growth enriches our understanding of intricate genetic characteristics, paving the way for advancements in modern breeding and precision agriculture. Within the domain of phenotyping, segmenting 3D point clouds of plant organ...
In aluminum electrolysis workshops, real-time pose perception of shelling heads is crucial to process accuracy and equipment safety. However, due to high temperatures, smoke, dust, and metal obstructions, traditional pose estimation methods struggle...
Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-regist...
Point clouds acquired with LiDAR are widely adopted in various fields, such as three-dimensional (3D) reconstruction, autonomous driving, and robotics. However, the high-density point cloud of large scenes captured with Lidar usually contains a large...
3D object-detection based on LiDAR point clouds can help driverless vehicles detect obstacles. However, the existing point-cloud-based object-detection methods are generally ineffective in detecting small objects such as pedestrians and cyclists. The...
Today, multi-sensor fusion detection frameworks in autonomous driving, especially sequence-based data-level fusion frameworks, face high latency and coupling issues and generally perform worse than LiDAR-only detectors. On this basis, we propose PMPF...
The detection of exposed subsea pipelines is a key task in current marine remote sensing, and multibeam echosounders (MBESs) are a primary instrument for detecting exposed pipelines. However, complex seabed environments interfere with acoustic echoes...
Point clouds with rich local geometric information have potentially huge implications in several applications, especially in areas of robotic manipulation and autonomous driving. However, most point cloud processing methods cannot extract enough geom...
Prior studies on indoor LiDAR point-cloud semantic segmentation consistently report that sampling density strongly affects segmentation accuracy as well as runtime and memory, establishing an accuracy–efficiency trade-off. Nevertheless, in prac...
Step-feature lines are one of the important geometrical elements for drawing the status quo maps of open-pit mines, and the efficient and accurate automatic extraction and updating of step-feature lines is of great significance for open-pit-mine stri...
The point cloud is the basis for 3D object surface reconstruction. An incomplete point cloud significantly reduces the accuracy of downstream work such as 3D object reconstruction and recognition. Therefore, point-cloud repair is indispensable work....
The increasing reliance on 3D scanning and meshless methods highlights the need for algorithms optimized for point-cloud geometry representations in CAE simulations. While voxel-based binning methods are simple, they often compromise geometry and top...
The generative adversarial network (GAN) has recently emerged as a promising generative model. Its application in the image field has been extensive, but there has been little research concerning point clouds.The combination of a GAN and a graph conv...
Total and automatic digitalization of indoor spaces in 3D implies a great advance in building maintenance and construction tasks, which currently require visits and manual works. Terrestrial laser scanners (TLS) have been widely used for these tasks,...
In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid t...
In this paper, we propose an efficient COordinate Rotation DIgital Computer (CORDIC) iteration circuit design for Light Detection and Ranging (LiDAR) sensors. A novel CORDIC architecture that achieves the goal of pre-selecting angles and reduces the...
In point-cloud scenes, semantic segmentation is the basis for achieving an understanding of a 3D scene. The disorderly and irregular nature of 3D point clouds makes it impossible for traditional convolutional neural networks to be applied directly, a...
Point cloud semantic segmentation remains challenging under extremely low annotation budgets due to inefficient utilization of sparse labels and sensitivity to data augmentation noise. To address this, we propose a dual-branch consistency learning (D...
In animal husbandry applications, segmenting live pigs in complex farming environments faces many challenges, such as when pigs lick railings and defecate within the acquisition environment. The pig’s behavior makes point cloud segmentation mor...
The point cloud segmentation method plays an important role in practical applications, such as remote sensing, mobile robots, and 3D modeling. However, there are still some limitations to the current point cloud data segmentation method when applied...
The construction of scene point-cloud maps is an important prerequisite for the registration-based localization of autonomous vehicles. In order to address the issues of the large cumulative error and low utilization efficiency of sensing information...
Point-cloud registration is a fundamental task in computer vision. However, most point clouds are partially overlapping, corrupted by noise and comprised of indistinguishable surfaces, especially for complexly distributed outdoor LiDAR point clouds,...
Light detection and ranging (LiDAR) has been widely used in navigation and environmental perception owing to its excellent beam directivity and high spatial resolution. Among its modalities, single-photon (photon-counting) LiDAR offers higher detecti...
The point cloud data from actual measurements are often sparse and incomplete, making it difficult to apply them directly to visual processing and 3D reconstruction. The point cloud completion task can predict missing parts based on a sparse and inco...
This paper proposes a point-by-point weighted fusion algorithm based on an improved random sample consensus (RANSAC) and inverse distance weighting to address the issue of low-resolution point cloud data obtained from light detection and ranging (LiD...
LiDAR (Light Detection And Ranging) technology is an important means to obtain three-dimensional information of trees and vegetation. However, due to the influence of scanning mode, environmental occlusion and mutual occlusion between tree canopies a...
Reconstructing 3D objects from single-view images has acquired significant interest due to their wide-ranging applications in robotics, autonomous vehicles, virtual reality, and augmented reality. Current methods, including voxel-based and point clou...
The advancements in the robotic field have made it possible for service robots to increasingly become part of everyday indoor scenarios. Their ability to operate and reach defined goals depends on the perception and understanding of their surrounding...
This study presents a critical evaluation of image-based 3D reconstruction techniques for small archaeological artifacts, focusing on a quantitative comparison between Neural Radiance Fields (NeRF), its recent Gaussian Splatting (GS) variant, and tra...
Accurate alignment of 3D point clouds, achieved by ubiquitous sensors such as LiDAR and depth cameras, is critical for enhancing perception capabilities in robotics, autonomous navigation, and environmental reconstruction. However, low-overlap scenar...
This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF)...
Reliable autonomy for drones operating in GNSS-intermittent or denied environments requires both stable inter-vehicle coordination and a shared global understanding of the environment. This paper presents a unified vision-based framework in which UAV...
In this paper, we face the point-cloud segmentation problem for spinning laser sensors from a deep-learning (DL) perspective. Since the sensors natively provide their measurements in a 2D grid, we directly use state-of-the-art models designed for vis...
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