A binary feature description and registration algorithm for a 3D point cloud based on retina-like sampling on projection planes (RSPP) are proposed in this paper. The algorithm first projects the point cloud within the support radius around the key p...
A significant amount of research has been conducted on the segmentation of large-scale 3D point clouds. However, efficient point cloud feature identification from segmentation results is an essential capability for computer vision and surveying tasks...
Existing methods for 3D local feature description often struggle to achieve a good balance between distinctiveness, robustness, and computational efficiency. To address this challenge, a novel 3D local feature descriptor named Histograms of Projected...
Local feature description of point clouds is essential in 3D computer vision. However, many local feature descriptors for point clouds struggle with inadequate robustness, excessive dimensionality, and poor computational efficiency. To address these...
As an essential part of point cloud processing, autonomous classification is conventionally used in various multifaceted scenes and non-regular point distributions. State-of-the-art point cloud classification methods mostly process raw point clouds,...
LiDAR point cloud object recognition plays an important role in robotics, remote sensing, and automatic driving. However, it is difficult to fully represent the object feature information only by using the point cloud information. To address this cha...
The challenges of point cloud registration in intelligent vehicle driving lie in the large scale, complex distribution, high noise, and strong sparsity of lidar point cloud data. This paper proposes an efficient registration algorithm for large-scale...
Nowadays, mobile robot exploration needs a rangefinder to obtain a large number of measurement points to achieve a detailed and precise description of a surrounding area and objects, which is called the point cloud. However, a single point cloud scan...
Three-dimensional (3D) Scene Understanding achieves environmental perception by extracting and analyzing point cloud data with wide applications including virtual reality, robotics, etc. Previous methods align the 2D image feature from a pre-trained...
In the fields of wind tunnel measurement and aerospace, the real-time pose information of aircraft is an important index. In this paper, we propose a large-scale aircraft pose estimation system, in which depth cameras are used to scan the entire airc...
With the development and popularization of LiDAR technology, point clouds are becoming widely used in multiple fields. Point cloud classification plays an important role in segmentation, geometric analysis, and vegetation description. However, existi...
Semantic segmentation of point clouds provided by airborne LiDAR survey in urban scenes is a great challenge. This is due to the fact that point clouds at boundaries of different types of objects are easy to be mixed and have geometric spatial simila...
Light Detection and Ranging (LiDAR), which applies light in the formation of a pulsed laser to estimate the distance between the LiDAR sensor and objects, is an effective remote sensing technology. Many applications use LiDAR including autonomous veh...
Establishing an effective local feature descriptor and using an accurate key point matching algorithm are two crucial tasks in recognizing and registering on the 3D point cloud. Because the descriptors need to keep enough descriptive ability against...
To address the issue of significant point cloud ghosting in the construction of high-precision point cloud maps by low-speed intelligent mobile vehicles due to the presence of numerous dynamic obstacles in the environment, which affects the accuracy...
To overcome the drawbacks of pairwise registration for mobile laser scanner (MLS) point clouds, such as difficulty in searching the corresponding points and inaccuracy registration matrix, a robust coarse-to-fine registration method is proposed to al...
Automated guided vehicles are widely used in warehousing environments for automated pallet handling, which is one of the fundamental parts to construct intelligent logistics systems. Pallet detection is a critical technology for automated guided vehi...
The simultaneous localization and mapping (SLAM) method estimates vehicles’ pose and builds maps established on the collection of environmental information primarily through sensors such as LiDAR and cameras. Compared to the camera-based SLAM,...
To address the registration problem in current machine vision, a new three-dimensional (3-D) point cloud registration algorithm that combines fast point feature histograms (FPFH) and greedy projection triangulation is proposed. First, the feature inf...
This study addresses the challenge of simplifying massive multibeam seafloor topographic point cloud datasets featuring significant spatial heterogeneity. We propose a feature joint entropy-based quantification method for seafloor terrain complexity,...
With the rapid advancement in laser scanning technologies, the capability to collect massive volumes of data and richer detailed features has been significantly enhanced. However, the differential representation ability of multi-source point clouds i...
Simplified models are widely applied in finite element computations regarding mechanical and structural problems. However, the simplified model sometimes causes many deviations in the finite element analysis (FEA) of structures, especially in the non...
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...
We propose a point cloud-based framework for open-vocabulary object pose estimation, called Pov9D. Existing approaches are predominantly RGB-based and often rely on texture or appearance cues, making them susceptible to pose ambiguities when objects...
Point cloud registration is a fundamental task in many applications such as localization, mapping, tracking, and reconstruction. Successful registration relies on extracting robust and discriminative geometric features. Though existing learning-based...
This paper shows an AI-assisted workflow supported by a Large Language Model for the geometric and informative reconstruction of masonry arch bridges from multi-source input data. The proposed methodology combines point cloud preprocessing, vegetatio...
Interpreting 3D point cloud data of the interior and exterior of buildings is essential for automated navigation, interaction and 3D reconstruction. However, the direct exploitation of the geometry is challenging due to inherent obstacles such as noi...
Object detection is important in many applications, such as autonomous driving. While 2D images lack depth information and are sensitive to environmental conditions, 3D point clouds can provide accurate depth information and a more descriptive enviro...
In agriculture, information about the spatial distribution of plant growth is valuable for applications. Quantitative study of the characteristics of plants plays an important role in the plants’ growth and development research, and non-destruc...
The robotic mapping problem, which consists in providing a spatial model of the environment to a robot, is a research topic with a wide range of applications. One important challenge of this problem is to obtain a map that is information-rich (i.e.,...
In the vehicle pose estimation task based on roadside Lidar in cooperative perception, the measurement distance, angle, and laser resolution directly affect the quality of the target point cloud. For incomplete and sparse point clouds, current method...
Pose estimation is a particularly important link in the task of robotic bin-picking. Its purpose is to obtain the 6D pose (3D position and 3D posture) of the target object. In real bin-picking scenarios, noise, overlap, and occlusion affect accuracy...
Light detection and ranging (LiDAR) has become a mainstream technique for rapid acquisition of 3-D geometry. Current LiDAR platforms can be mainly categorized into spaceborne LiDAR system (SLS), airborne LiDAR system (ALS), mobile LiDAR system (MLS),...
Addressing the challenge of stable cloud velocity field estimation under complex sky conditions in ground-based cloud imaging, this paper proposes a comprehensive 3D cloud velocity calculation framework. The methodology integrates binocular stereo vi...
Phenotypic analysis has always played an important role in breeding research. At present, wheat phenotypic analysis research mostly relies on high-precision instruments, which make the cost higher. Thanks to the development of 3D reconstruction techn...
Simultaneous localization and mapping (SLAM) algorithm is a prerequisite for unmanned ground vehicle (UGV) localization, path planning, and navigation, which includes two essential components: frontend odometry and backend optimization. Frontend odom...
Positioning information has become one of the most important information for processing and displaying on smart mobile devices. In this paper, we propose a visual positioning method using RGB-D image on smart mobile devices. Firstly, the pose of each...
At present, PPF-based point cloud recognition algorithms can perform better matching than competitors and be verified in the case of severe occlusion and stacking. However, including certain superfluous feature point pairs in the global model descrip...
Point cloud registration (PCR) is a vital problem in remote sensing and computer vision, which has various important applications, such as 3D reconstruction, object recognition, and simultaneous localization and mapping (SLAM). Although scholars have...
Ordered grasping in stacked-tabletop scenes requires a robot to infer both local grasp poses and object-support order. This paper presents an annotation-guided geometric support-reasoning framework based on multi-view red–green–blue&ndash...
It is still a completely new and challenging task to register extensive, enormous and sparse satellite light detection and ranging (LiDAR) point clouds. Aimed at this problem, this study provides a ridgeline-based terrain co-registration method in pr...
Introducing deep learning methods into the quality inspection of production lines can reduce labor and improve efficiency, with great potential for the development of manufacturing systems. However, in specific closed production-line environments, ro...
Loop closure detection (LCD) can effectively eliminate the cumulative errors in simultaneous localization and mapping (SLAM) by detecting the position of a revisit and building interframe pose constraint relations. However, in real-world natural scen...
Automatic visual inspection allows for the identification of surface defects in manufactured parts. Nevertheless, when defects are on a sub-millimeter scale, detection and recognition are a challenge. This is particularly true when the defect generat...
Indoor positioning is in high demand in a variety of applications, and indoor environment is a challenging scene for visual positioning. This paper proposes an accurate visual positioning method for smartphones. The proposed method includes three pro...
In the field of digital cultural heritage (DCH), 2D/3D digitization strategies are becoming more and more complex. The emerging trend of multimodal imaging (i.e., data acquisition campaigns aiming to put in cooperation multi-sensor, multi-scale, mult...
The use of a 3D range scanning device for autonomous object description or unknown environment mapping leads to the necessity of improving computer methods based on identical point pairs from different point clouds (so-called registration problem). T...
Gardens play a key role in the definition of the cultural landscape since they reflect the culture, identity, and history of a people. They also contribute to the ecological balance of the city. Despite the fact that gardens have an historic and soci...
Autonomous mobile robots (AMRs) that depend on remote inference introduce communication and service availability into the navigation control path. This paper presents a cloud-independent mapless-navigation architecture integrating visual features dis...
An accurate ego-motion estimation solution is vital for autonomous vehicles. LiDAR is widely adopted in self-driving systems to obtain depth information directly and eliminate the influence of changing illumination in the environment. In LiDAR odomet...
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