Results 31 to 40 of about 496,863 (282)

DTM GENERATION WITH UAV BASED PHOTOGRAMMETRIC POINT CLOUD [PDF]

open access: yesThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
Nowadays Unmanned Aerial Vehicles (UAVs) are widely used in many applications for different purposes. Their benefits however are not entirely detected due to the integration capabilities of other equipment such as; digital camera, GPS, or laser scanner ...
N. Polat, M. Uysal
doaj   +1 more source

Analysis of Point Cloud Generation from UAS Images [PDF]

open access: yesISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014
Unmanned Aerial Systems (UAS) allow for the collection of low altitude aerial images, along with other geospatial information from a variety of companion sensors.
S. Ostrowski   +3 more
doaj   +1 more source

VIDEO-BASED POINT CLOUD GENERATION USING MULTIPLE ACTION CAMERAS [PDF]

open access: yesThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
Due to the development of action cameras, the use of video technology for collecting geo-spatial data becomes an important trend. The objective of this study is to compare the image-mode and video-mode of multiple action cameras for 3D point clouds ...
T. Teo
doaj   +1 more source

Development of Improved Semi-Automated Processing Algorithms for the Creation of Rockfall Databases

open access: yesRemote Sensing, 2021
While terrestrial laser scanning and photogrammetry provide high quality point cloud data that can be used for rock slope monitoring, their increased use has overwhelmed current data analysis methodologies.
Heather Schovanec   +3 more
doaj   +1 more source

Accuracy assessment of building point clouds automatically generated from iphone images [PDF]

open access: yesThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014
Low-cost sensor generated 3D models can be useful for quick 3D urban model updating, yet the quality of the models is questionable. In this article, we evaluate the reliability of an automatic point cloud generation method using multi-view iPhone images
B. Sirmacek, R. Lindenbergh
doaj   +1 more source

Attention-Based Dense Point Cloud Reconstruction From a Single Image

open access: yesIEEE Access, 2019
Three-dimensional Reconstruction has drawn much attention in computer vision. Generating a dense point cloud from a single image is a more challenging task.
Qiang Lu   +4 more
doaj   +1 more source

3D point cloud generation reconstruction from single image based on image retrieval

open access: yesResults in Optics, 2021
Most of the 3D point cloud generation methods from a single image are only applicable to a single target. However, the real image often contains multiple targets, which is difficult to generate 3D point clouds from the network directly.
Hui Chen, Yipeng Zuo, Yong Tong, Li Zhu
doaj   +1 more source

UAV-BASED POINT CLOUD GENERATION FOR OPEN-PIT MINE MODELLING [PDF]

open access: yesThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
Along with the advancement of unmanned aerial vehicles (UAVs), improvement of high-resolution cameras and development of vision-based mapping techniques, unmanned aerial imagery has become a matter of remarkable interest among researchers and industries.
M. Shahbazi   +3 more
doaj   +1 more source

New instruments and technologies for Cultural Heritage survey: full integration between point clouds and digital photogrammetry [PDF]

open access: yes, 2010
In the last years the Geomatic Research Group of the Politecnico di Torino faced some new research topics about new instruments for point cloud generation (e.g. Time of Flight cameras) and strong integration between multi-image matching techniques and 3D
Chiabrando, Filiberto   +3 more
core   +1 more source

Learning to Generate Realistic LiDAR Point Clouds

open access: yes, 2022
We present LiDARGen, a novel, effective, and controllable generative model that produces realistic LiDAR point cloud sensory readings. Our method leverages the powerful score-matching energy-based model and formulates the point cloud generation process as a stochastic denoising process in the equirectangular view. This model allows us to sample diverse
Zyrianov, Vlas   +2 more
openaire   +2 more sources

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