Results 41 to 50 of about 18,303 (165)
3D Point Cloud Recognition Based on a Multi-View Convolutional Neural Network
Sensors, 2018 The recognition of three-dimensional (3D) lidar (light detection and ranging) point clouds remains a significant issue in point cloud processing. Traditional point cloud recognition employs the 3D point clouds from the whole object.
Le Zhang, Jian Sun, Qiang Zheng
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Point attention network for semantic segmentation of 3D point clouds [PDF]
Pattern Recognition, 2020 Submitted to a ...
Mingtao Feng +4 more
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Adversarial Shape Perturbations on 3D Point Clouds [PDF]
, 2020 18 pages, accepted to the 2020 ECCV workshop on Adversarial Robustness in the Real World, source code available at this https url: https://github.com/Daniel-Liu-c0deb0t/Adversarial-point-perturbations-on-3D ...
Daniel Liu, Ronald Yu, Hao Su 0001
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Feature Visualization for 3D Point Cloud Autoencoders
2020 International Joint Conference on Neural Networks (IJCNN), 2020 In order to reduce the dimensionality of 3D point cloud representations, autoencoder architectures generate increasingly abstract, compressed features of the input data. Visualizing these features is central to understanding the learning process, however, while successful visualization techniques exist for neural networks applied to computer vision ...
Thiago Rios +5 more
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Global Journal of Engineering and Technology Advances, 2023 A collection of multidimensional points is represented by a data structure called a point cloud, which is frequently used to describe 3-D data. A point cloud is, technically speaking, a database of points in a three-dimensional coordinate system. However, from the viewpoint of a typical workflow, the only thing that matters is that a point cloud is an ...
Roopa B S +3 more
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3D DATA ACQUISITION FOR INDOOR ASSETS USING TERRESTRIAL LASER SCANNING [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013 The newly development of technology clearly shows an improvement of three-dimension (3D) data acquisition techniques. The requirements of 3D information and features have been obviously increased during past few years in many related fields.
S. Y. Lee, Z. Majid, H. Setan
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3D CITY MODELLING OF ISTANBUL BASED ON LIDAR DATA AND PANORAMIC IMAGES – ISSUES AND CHALLENGES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 This paper describes the generation of 3D city modelling of LoD2 and LoD3 buildings based on 3D point clouds data and other auxiliary data for Istanbul city, Turkey.
G. Buyuksalih +4 more
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Unsupervised Domain Adaptation for 3D Point Clouds by Searched Transformations
IEEE Access, 2022 Input-level domain adaptation reduces the burden of a neural encoder without supervision by reducing the domain gap at the input level. Input-level domain adaptation is widely employed in 2D visual domain, e.g., images and videos, but is not utilized for
Dongmin Kang +3 more
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Remote SensingTraditional single-channel Synthetic Aperture Radar (SAR) cannot achieve high-resolution and wide-swath (HRWS) imaging due to the constraint of the minimum antenna area.
Yuling Liu +3 more
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A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning
Remote Sensing, 2022 Three-dimensional digital models play a pivotal role in city planning, monitoring, and sustainable management of smart and Digital Twin Cities (DTCs). In this context, semantic segmentation of airborne 3D point clouds is crucial for modeling, simulating,
Zouhair Ballouch +4 more
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