Parametric splines are popular tools for precision optical metrology of complex freeform surfaces. However, as a promising topologically unconstrained solution, existing T-spline fitting techniques, such as improved global fitting, local fitting, and...
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...
Existing 3D reconstruction algorithms commonly struggle with modeling specific local objects within large-scale scenes, often resulting in a lack of local detail and incomplete geometric structures. While current mainstream point cloud completion met...
As 3D acquisition equipment picks up steam, point cloud registration has been applied in ever-increasing fields. This paper provides an exhaustive survey of the field of point cloud registration for laser scanners and examines its application in larg...
With the proliferation of large-scale 3D point cloud datasets, the high cost of per-point annotation has spurred the development of weakly supervised semantic segmentation methods. Current popular research mainly focuses on single-scale classificatio...
In recent years, large-scale point cloud semantic segmentation has been widely applied in various fields, such as remote sensing and autonomous driving. Most existing point cloud networks use local aggregation to abstract unordered point clouds layer...
The normal vector estimation of the large-scale scattered point cloud (LSSPC) plays an important role in point-based shape editing. However, the normal vector estimation for LSSPC cannot meet the great challenge of the sharp increase of the point clo...
Signals, such as point clouds captured by light detection and ranging sensors, are often affected by highly reflective objects, including specular opaque and transparent materials, such as glass, mirrors, and polished metal, which produce reflection...
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...
This paper introduces an efficient 3D point cloud downsampling algorithm (DFPS) based on adaptive multi-level grid partitioning. By leveraging an adaptive hierarchical grid partitioning mechanism, the algorithm dynamically adjusts computational inten...
Remote sensing technology has found extensive application in agriculture, providing critical data for analysis. The advancement of semantic segmentation models significantly enhances the utilization of point cloud data, offering innovative technical...
We present a fully automatic approach for reconstructing compact 3D building models from large-scale airborne point clouds. A major challenge of urban reconstruction from airborne LiDAR point clouds lies in that the vertical walls are typically missi...
Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand the scene environment in various applications, such as geometry mapping, autonomous driving, and more. With an advantage of being a 3D metric space, 3D...
The accurate and efficient segmentation of large-scale urban point clouds is crucial for many higher-level tasks, such as boundary line extraction, point cloud registration, and deformation measurement. In this paper, we propose a novel supervoxel se...
In this research, we introduce a deep-learning-based framework designed for the prediction of transonic flow through a linear cascade utilizing large-scale point-cloud data. In our experimental cases, the predictions demonstrate a nearly four-fold sp...
High-density point clouds are valuable and detailed sources of data for different processes related to photogrammetry. We explore the knowledge-based generation of accurate large-scale three-dimensional (3D) models of buildings employing point clouds...
The digital documentation and analysis of cultural heritage increasingly rely on high-precision three-dimensional point cloud data, which often suffers from missing regions due to limitations in acquisition conditions, hindering subsequent analyses a...
The accuracy with which a neural network interprets a point cloud depends on the quality of the features expressed by the network. Addressing this issue, we propose a multi-level feature extraction layer (MFEL) which collects local contextual feature...
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...
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...
Accurate semantic segmentation of 3D point clouds is a long-standing problem in remote sensing and computer vision. Due to the unstructured nature of point clouds, designing deep neural architectures for point cloud semantic segmentation is often not...
The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. Th...
The semantic segmentation of point clouds is a crucial undertaking in 3D reconstruction and holds great importance. However, achieving precise semantic segmentation represents a significant hurdle. In this paper, we present BEMF-Net, an efficient met...
Real-time large-scale point cloud segmentation is an important but challenging task for practical applications such as remote sensing and robotics. Existing real-time methods have achieved acceptable performance by aggregating local information. Howe...
This article presents a novel fully automatic indoor surveying (FAIS) framework for large-scale indoor environments using a Terrestrial Laser Scanning (TLS) hardware system. Traditional methods for indoor surveying are labor-intensive and time-consum...
Leveraging the open-world understanding capacity of large-scale visual-language pre-trained models has become a hot spot in point cloud classification. Recent approaches rely on transferable visual-language pre-trained models, classifying point cloud...
Spherical targets are widely used in coordinate unification of large-scale combined measurements. Through its central coordinates, scanned point cloud data from different locations can be converted into a unified coordinate reference system. However,...
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...
Point cloud registration plays a great role in many application scenarios; however, the registration of large-scale point clouds for actual different moments suffers from the problems of low efficiency, low accuracy, and a lack of stability. In this...
Automatic large-scale building extraction from the LiDAR point clouds and remote sensing images is a growing focus in the fields of the sensor applications and remote sensing. However, this building extraction task remains highly challenging due to t...
Due to the significant bandwidth and memory requirements for transmitting and storing large-scale point clouds, considerable progress has been made in recent years in the field of large-scale point cloud geometry compression. However, challenges rema...
3D point cloud classification has wide applications in the field of scene understanding. Point cloud classification based on points can more accurately segment the boundary region between adjacent objects. In this paper, a point cloud classification...
There are some irregular and disordered noise points in large-scale point clouds, and the accuracy of existing large-scale point cloud classification methods still needs further improvement. This paper proposes a network named MFTR-Net, which conside...
Point cloud semantic segmentation is essential for comprehending and analyzing scenes. However, performing semantic segmentation on large-scale point clouds presents challenges, including demanding high memory requirements, a lack of structured data,...
In this paper, an optimized three-dimensional (3D) pairwise point cloud registration algorithm is proposed, which is used for flatness measurement based on a laser profilometer. The objective is to achieve a fast and accurate six-degrees-of-freedom (...
Accurate segmentation of point clouds into categories such as roads, buildings, and trees is critical for applications in 3D reconstruction and autonomous driving. However, large-scale point cloud segmentation encounters challenges such as uneven den...
Semantic segmentation of large-scale indoor 3D point cloud scenes is crucial for scene understanding but faces challenges in effectively modeling long-range dependencies and multi-scale features. In this paper, we present RegionPVT, a novel Regional-...
With the use of terrestrial laser scanning (TLS) in forest stands, surveys are now equipped to obtain dense point cloud data. However, the data range, i.e., the number of points, often reaches the billions or even higher, exceeding random access memo...
Over the past few years, various research has been conducted to utilize 3D point cloud data in construction sites. This is because 3D point cloud data contain a variety of information, such as spatial coordinates (X, Y, Z), intensity, and color (RGB)...
With the continuous expansion of power grids, traditional manual inspection methods face numerous challenges, including low efficiency, high costs, and significant safety risks. As critical infrastructure in power transmission systems, power grid tow...
In this study, we introduce a novel framework for the semantic segmentation of point clouds in autonomous driving scenarios, termed PVI-Net. This framework uniquely integrates three different data perspectives—point clouds, voxels, and distance...
Background: The development of laser measurement techniques is of great significance in forestry monitoring and park management in smart cities. It provides many conveniences for improving landscape planning efficiency and strengthening digital const...
At the present time, many publicly available point cloud datasets exist, which are mainly focused on autonomous driving. The objective of this study is to develop a new large-scale mobile 3D LiDAR point cloud dataset for outdoor scene semantic segmen...
Despite the fact that point cloud registration under noisy conditions has recently begun to be tackled by several non-correspondence algorithms, they neither struggle to fuse the global features nor abandon early state estimation during the iterative...
Ground filtering (GF) is a fundamental step for airborne laser scanning (ALS) data processing. The advent of deep learning techniques provides new solutions to this problem. Existing deep-learning-based methods utilize a segmentation or classificatio...
The classification and segmentation of large-scale, sparse, LiDAR point cloud with deep learning are widely used in engineering survey and geoscience. The loose structure and the non-uniform point density are the two major constraints to utilize the...
Recently, new semantic segmentation and object detection methods have been proposed for the direct processing of three-dimensional (3D) LiDAR sensor point clouds. LiDAR can produce highly accurate and detailed 3D maps of natural and man-made environm...
Collaborative sensing between low-altitude remote sensing and ground-based mobile mapping lays the theoretical foundation for multi-platform 3D data fusion. However, point clouds collected from Airborne Laser Scanners (ALSs) remain scarce due to high...
Object recognition algorithms and datasets based on point cloud data have been mainly designed for autonomous vehicles. When applied to the construction industry, they face challenges due to the origin of point cloud data from large earthwork sites,...
This paper presents an effective and semi-automated method for detecting 3D edges in 3D point clouds with the help of high-resolution digital images. The effort aims to contribute towards addressing the unsolved problem of automated production of vec...
of 11