Results 21 to 30 of about 4,650,387 (294)
Façade structure reconstruction using spaceborne TomoSAR point clouds [PDF]
, 2012 Very high resolution SAR tomography using multiple data stacks from different viewing angles enables us for the first time to generate 4D point clouds of the illuminated area from space with a point density comparable to LiDAR.
Muhammad Shahzad +5 more
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Enriching Thermal Point Clouds of Buildings using Semantic 3D building Models [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information SciencesThermal point clouds integrate thermal radiation and laser point clouds effectively. However, the semantic information for the interpretation of building thermal point clouds can hardly be precisely inferred. Transferring the semantics encapsulated in 3D
J. Zhu +4 more
doaj +1 more source
Plane-based Coarse Registration of 3D Point Clouds with 4D Models [PDF]
, 2011 The accurate registration of 3D point clouds with project 3D/4D models is becoming more and more important with the development of BIM and 3D laser scanning, for which the registration in a common coordinate system is critical to project control.
Bosché, Frédéric, Frederic Bosche
core +1 more source
Remote Sensing, 2021 Estimation of urban tree canopy parameters plays a crucial role in urban forest management. Unmanned aerial vehicles (UAV) have been widely used for many applications particularly forestry mapping.
Ebadat Ghanbari Parmehr, Marco Amati
doaj +1 more source
CoRR, 2018 Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data. In this paper, we first show a straightforward extension of existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data.
Chun-Liang Li +4 more
openaire +3 more sources
DPDist: Comparing Point Clouds Using Deep Point Cloud Distance [PDF]
, 2020 We introduce a new deep learning method for point cloud comparison. Our approach, named Deep Point Cloud Distance (DPDist), measures the distance between the points in one cloud and the estimated surface from which the other point cloud is sampled. The surface is estimated locally and efficiently using the 3D modified Fisher vector representation.
Dahlia Urbach +2 more
openaire +2 more sources
, 2021 Both geometric and semantic information are required for a complete understanding of regions acquired as three-dimensional (3D) point clouds using the Light Detection and Ranging (LiDAR) technology.
Sreevalsan-Nair, Jaya +2 more
core +1 more source
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
doaj +1 more source
QUALIFICATION OF POINT CLOUDS MEASURED BY SFM SOFTWARE [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 This paper proposes a qualification method of a point cloud created by SfM (Structure-from-Motion) software. Recently, SfM software is popular for creating point clouds.
K. Oda +4 more
doaj +1 more source
Predictive point-cloud compression [PDF]
ACM SIGGRAPH 2005 Sketches on - SIGGRAPH '05, 2005 Point clouds have recently become a popular alternative to polygonal meshes for representing three-dimensional geometric models. 3D photography and scanning systems acquire the geometry and appearance of real-world objects in form of point samples.
Gumhold, S. +3 more
openaire +3 more sources