scholarly journals Fast Surface Reconstruction and Segmentation with Ground-Based and Airborne LIDAR Range Data

2009 ◽  
Author(s):  
Matthew Carlberg ◽  
James Andrews ◽  
Peiran Gao ◽  
Avideh Zakhor
Keyword(s):  
Surface Reconstruction ◽  
Airborne Lidar ◽  
Range Data

Accurate surface reconstruction of large 3D objects from range data

2003 ◽  
Author(s):  
Xiaokun Li ◽  
Lei He ◽  
Bryan Everding ◽  
Chia Y. Han ◽  
William G. Wee
Keyword(s):  
Surface Reconstruction ◽  
Range Data ◽  
3D Objects

Capturing tree crown formation through implicit surface reconstruction using airborne lidar data

2009 ◽  
Vol 113 (6) ◽  
pp. 1148-1162 ◽  
Author(s):  
Akira Kato ◽  
L. Monika Moskal ◽  
Peter Schiess ◽  
Mark E. Swanson ◽  
Donna Calhoun ◽  
...  
Keyword(s):  
Surface Reconstruction ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Implicit Surface ◽  
Tree Crown ◽  
Crown Formation ◽  
Airborne Lidar Data

scholarly journals Improving the Accuracy of Automatic Reconstruction of 3D Complex Buildings Models from Airborne Lidar Point Clouds

2020 ◽  
Vol 12 (10) ◽  
pp. 1643 ◽  
Author(s):  
Marek Kulawiak ◽  
Zbigniew Lubniewski
Keyword(s):  
Surface Reconstruction ◽  
Spatial Data ◽  
Measurement Data ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Lidar ◽  
Reconstruction Method ◽  
Cloud Data ◽  
Reconstruction Methods

Due to high requirements of variety of 3D spatial data applications with respect to data amount and quality, automatized, efficient and reliable data acquisition and preprocessing methods are needed. The use of photogrammetry techniques—as well as the light detection and ranging (LiDAR) automatic scanners—are among attractive solutions. However, measurement data are in the form of unorganized point clouds, usually requiring transformation to higher order 3D models based on polygons or polyhedral surfaces, which is not a trivial process. The study presents a newly developed algorithm for correcting 3D point cloud data from airborne LiDAR surveys of regular 3D buildings. The proposed approach assumes the application of a sequence of operations resulting in 3D rasterization, i.e., creation and processing of a 3D regular grid representation of an object, prior to applying a regular Poisson surface reconstruction method. In order to verify the accuracy and quality of reconstructed objects for quantitative comparison with the obtained 3D models, high-quality ground truth models were used in the form of the meshes constructed from photogrammetric measurements and manually made using buildings architectural plans. The presented results show that applying the proposed algorithm positively influences the quality of the results and can be used in combination with existing surface reconstruction methods in order to generate more detailed 3D models from LiDAR scanning.

scholarly journals Robust and Efficient Surface Reconstruction From Range Data

2009 ◽  
Vol 28 (8) ◽  
pp. 2275-2290 ◽  
Author(s):  
P. Labatut ◽  
J.-P. Pons ◽  
R. Keriven
Keyword(s):  
Surface Reconstruction ◽  
Range Data
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