Results 21 to 30 of about 309,652 (283)
Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation [PDF]
, 2020 Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait ...
Luo, Jian, Tjahjadi, Tardi
core +1 more source
Leaves Segmentation in 3D Point Cloud [PDF]
, 2017 This paper presents a 3D plant segmentation method with an emphasis on segmentation of the leaves. This method is part of a 3D plant phenotyping project with a main objective that deals with the development of the leaf area over time. First, a 3D point cloud of a plant is obtained with Structure from Motion technique and the cloud is then segmented ...
Gelard, William +6 more
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General Hypernetwork Framework for Creating 3D Point Clouds [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 In this work, we propose a novel method for generating 3D point clouds that leverages the properties of hypernetworks. Contrary to the existing methods that learn only the representation of a 3D object, our approach simultaneously finds a representation of the object and its 3D surface.
Przemyslaw Spurek +3 more
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Remote Sensing, 2020 The preference of three-dimensional representation of underground cable wells from two-dimensional symbols is a developing trend, and three-dimensional (3D) point cloud data is widely used due to its high precision.
Ming Huang +4 more
doaj +1 more source
DENOISING OF 3D POINT CLOUDS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 Abstract. A method to remove random errors from 3D point clouds is proposed. It is based on the estimation of a local geometric descriptor of each point. For mobile mapping LiDAR and airborne LiDAR, a combined standard mesurement uncertainty of the LiDAR system may supplement a geometric approach.
E. Mugner, N. Seube
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GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification
Applied Sciences, 2022 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.
Long Hoang +3 more
doaj +1 more source
3D point cloud lossy compression using quadric surfaces [PDF]
PeerJ Computer Science, 2021 The presence of 3D sensors in hand-held or head-mounted smart devices has motivated many researchers around the globe to devise algorithms to manage 3D point cloud data efficiently and economically. This paper presents a novel lossy compression technique
Ulfat Imdad +3 more
doaj +2 more sources
Machine Learning in LiDAR 3D Point Clouds [PDF]
, 2021 LiDAR point clouds contain measurements of complicated natural scenes and can be used to update digital elevation models, glacial monitoring, detecting faults and measuring uplift detecting, forest inventory, detect shoreline and beach volume changes, landslide risk analysis, habitat mapping, and urban development, among others.
Medina, F. Patricia, Paffenroth, Randy
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Geometric 3D point cloud compression [PDF]
Pattern Recognition Letters, 2014 This work has been supported by the Spanish Government DPI2013-40534-R grant.
Morell, Vicente +3 more
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Hierarchical Optimization of 3D Point Cloud Registration [PDF]
Sensors, 2020 Rigid registration of 3D point clouds is the key technology in robotics and computer vision. Most commonly, the iterative closest point (ICP) and its variants are employed for this task. These methods assume that the closest point is the corresponding point and lead to sensitivity to the outlier and initial pose, while they have poor computational ...
Liu, Huikai +5 more
openaire +3 more sources