As one of the best means of obtaining the geometry information of special shaped structures, point cloud data acquisition can be achieved by laser scanning or photogrammetry. However, there are some differences in the quantity, quality, and informati...
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 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...
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)...
To address the challenges in industrial precision component detection posed by existing point cloud datasets, this research endeavors to amass and construct a point cloud dataset comprising 1101 models of miniature gears. The data collection and proc...
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...
The application of 3D scenes has gradually expanded in recent years. A 3D point cloud is unreliable when it is acquired because of the performance of the sensor. Therefore, it causes difficulties in utilization. Point cloud completion can reconstruct...
Point normal calculation and cloud registration are two of the most common operations in point cloud processing. However, both are vulnerable to issues of numerical precision and loss of significance. This paper documents how loss of significance in...
Point cloud registration is widely used in autonomous driving, SLAM, and 3D reconstruction, and it aims to align point clouds from different viewpoints or poses under the same coordinate system. However, point cloud registration is challenging in com...
Point cloud data, with its rich information and high-precision geometric details, holds significant value for urban road infrastructure surveying and management. To overcome the limitations of manual classification, this study employs deep learning t...
Point cloud registration is a key problem in computer vision applications and involves finding a rigid transform from a point cloud into another such that they align together. The iterative closest point (ICP) method is a simple and effective solutio...
The traditional Iterative Closest Point (ICP) algorithm often suffers from low computational accuracy and efficiency in certain scenarios. It is highly sensitive to the initial pose, has a poor ability to resist interference, and frequently becomes t...
With the development of the laser scanning technique, it is easier to obtain 3D large-scale scene rapidly. However, many scanned objects may suffer serious incompletion caused by the scanning angles or occlusion, which has severely impacted their fut...
Existing point cloud registration methods can effectively handle large-scale and partially overlapping point cloud pairs. However, registering unbalanced point cloud pairs with significant disparities in spatial extent and point density remains a cha...
Point cloud registration serves as a critical tool for constructing 3D environmental maps. Both geometric and color information are instrumental in differentiating diverse point features. Specifically, when points appear similar based solely on geome...
The point cloud is disordered and unstructured, and it is difficult to extract detailed features. The detailed part of the target shape is difficult to complete in the point cloud completion task. It proposes a point cloud completion network (BCA-Net...
In this paper, we propose two novel AR glasses pose estimation algorithms from single infrared images by using 3D point clouds as an intermediate representation. Our first approach “PointsToRotation” is based on a Deep Neural Network alone, whereas o...
The segmentation of 3D point clouds of plant organs, such as leaves and stems, helps to monitor plant growth and is a key step in plant growth phenotype analysis. Compared to point cloud segmentation tasks in other fields, plant point cloud segmentat...
Point cloud completion is a necessary task in real-world applications of recovering a complete geometry from missing regions of 3D objects. Furthermore, model efficiency is of vital importance in computer vision. In this paper, we present an efficien...
There are many sources of point cloud data, such as the point cloud model obtained after a bundle adjustment of aerial images, the point cloud acquired by scanning a vehicle-borne light detection and ranging (LiDAR), the point cloud acquired by terre...
Because of low accuracy and density of crop point clouds obtained by the Unmanned Aerial Vehicle (UAV)-borne Light Detection and Ranging (LiDAR) scanning system of UAV, an integrated navigation and positioning optimization method based on the grassho...
Analyzing point clouds with neural networks is a current research hotspot. In order to analyze the 3D geometric features of point clouds, most neural networks improve the network performance by adding local geometric operators and trainable parameter...
Occlusions are one of the leading causes of data degradation in lidar. The presence of occlusions reduces the overall aesthetic quality of a point cloud, creating a signature that is specific to that viewpoint and sensor modality. Typically, datasets...
Point cloud upsampling algorithms can improve the resolution of point clouds and generate dense and uniform point clouds, and are an important image processing technology. Significant progress has been made in point cloud upsampling research in recen...
Point cloud intelligence integrates advanced technologies such as Light Detection and Ranging (LiDAR), photogrammetry, and Artificial Intelligence (AI) to transform transportation infrastructure management. This review highlights state-of-the-art adv...
Automotive radars are essential environmental sensors due to their long sensing range and robustness under adverse conditions such as fog and poor visibility. However, their low point cloud resolution compared to LiDAR makes accurate object detection...
Airborne laser scanning (ALS) point cloud classification is a challenge due to factors including complex scene structure, various densities, surface morphology, and the number of ground objects. A point cloud classification method is presented in thi...
The emergence of a photogrammetry-based 3D reconstruction technique enables rapid 3D modeling at a low cost and uncovers many applications in documenting the geometric dimensions of the environment. Although the theoretical accuracy of photogrammetry...
In the last stage of colored point cloud registration, depth measurement errors hinder the achievement of accurate and visually plausible alignments. Recently, an algorithm has been proposed to extend the Iterative Closest Point (ICP) algorithm to re...
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...
LiDAR technology has been widely used in forest survey and research, but the high-resolution point cloud data generated by LiDAR equipment also pose challenges in storage and computing. To address this problem, we propose a point cloud simplification...
Deep point cloud neural networks have achieved promising performance in remote sensing applications, and the prevalence of Transformer in natural language processing and computer vision is in stark contrast to underexplored point-based methods. In th...
The rapid growth in technologies for 3D sensors has made point cloud data increasingly available in different applications such as autonomous driving, robotics, and virtual and augmented reality. This raises a growing need for deep learning methods t...
3D human action recognition is crucial in broad industrial application scenarios such as robotics, video surveillance, autonomous driving, or intellectual education, etc. In this paper, we present a new point cloud sequence network called PointMapNet...
For this study, a new point cloud alignment method is proposed for extracting corner points and aligning them at the geometric level. It can align point clouds that have low overlap and is more robust to outliers and noise. First, planes are extracte...
In 3D reconstruction applications, an important issue is the matching of point clouds corresponding to different perspectives of a particular object or scene, which is addressed by the use of variants of the Iterative Closest Point (ICP) algorithm. I...
A multispectral light detection and ranging (LiDAR) system, which simultaneously collects spatial geometric data and multi-wavelength intensity information, opens the door to three-dimensional (3-D) point cloud classification and object recognition....
As the three-dimensional (3D) laser scanner is widely used for forest inventory, analyzing and processing point cloud data captured with a 3D laser scanner have become an important research topic in recent years. The extraction of single trees from p...
Point clouds are essential 3D data representations utilized across various disciplines, often requiring point cloud completion methods to address inherent incompleteness. Existing completion methods like SnowflakeNet only consider local attention, la...
Point cloud collection forming a 3D scene typically uses information from multiple data scans. The common approach is to register the point cloud pairs consequentially using a variant of the iterative closest point (ICP) algorithm, but most versions...
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...
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...
Most 3D point cloud watermarking techniques apply Principal Component Analysis (PCA) to protect the watermark against affine transformation attacks. Unfortunately, they fail in the case of cropping and random point removal attacks. In this work, an a...
Laser point clouds are commonly affected by Gaussian and Laplace noise, resulting in decreased accuracy in subsequent surface reconstruction and visualization processes. However, existing point cloud denoising algorithms often overlook the local cons...
Recently, significant advancements have been made in 3D point cloud analysis by leveraging transformer architecture in 3D space. However, it remains challenging to effectively implement local and global learning within irregular and sparse structures...
To further improve the efficiency and accuracy of the vehicle part inspection process, this paper designs an accurate and efficient vehicle body part inspection framework based on scattered point cloud data (PCD). Firstly, a hybrid filtering algorith...
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