Complete 3D Surface Reconstruction from an Unstructured Point Cloud of Arbitrary Shape by Using a Bounding Voxel Model

2006 ◽  
Vol 30 (8) ◽  
pp. 906-915
Author(s):  
Rixie Li ◽  
Seok-Il Kim
Keyword(s):  
Surface Reconstruction ◽  
Arbitrary Shape ◽  
Point Cloud ◽  
3D Surface Reconstruction ◽  
3D Surface ◽  
Voxel Model

scholarly journals REGION-BASED 3D SURFACE RECONSTRUCTION USING IMAGES ACQUIRED BY LOW-COST UNMANNED AERIAL SYSTEMS

2015 ◽  
Vol XL-1/W4 ◽  
pp. 167-173 ◽  
Author(s):  
Z. Lari ◽  
A. Al-Rawabdeh ◽  
F. He ◽  
A. Habib ◽  
N. El-Sheimy
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Low Cost ◽  
Geospatial Data ◽  
Unmanned Aerial Systems ◽  
3D Surface Reconstruction ◽  
Planar Surfaces ◽  
3D Surface ◽  
Dense Matching ◽  
Aerial Systems

Accurate 3D surface reconstruction of our environment has become essential for an unlimited number of emerging applications. In the past few years, Unmanned Aerial Systems (UAS) are evolving as low-cost and flexible platforms for geospatial data collection that could meet the needs of aforementioned application and overcome limitations of traditional airborne and terrestrial mobile mapping systems. Due to their payload restrictions, these systems usually include consumer-grade imaging and positioning sensor which will negatively impact the quality of the collected geospatial data and reconstructed surfaces. Therefore, new surface reconstruction surfaces are needed to mitigate the impact of using low-cost sensors on the final products. To date, different approaches have been proposed to for 3D surface construction using overlapping images collected by imaging sensor mounted on moving platforms. In these approaches, 3D surfaces are mainly reconstructed based on dense matching techniques. However, generated 3D point clouds might not accurately represent the scanned surfaces due to point density variations and edge preservation problems. In order to resolve these problems, a new region-based 3D surface renostruction trchnique is introduced in this paper. This approach aims to generate a 3D photo-realistic model of individually scanned surfaces within the captured images. This approach is initiated by a Semi-Global dense Matching procedure is carried out to generate a 3D point cloud from the scanned area within the collected images. The generated point cloud is then segmented to extract individual planar surfaces. Finally, a novel region-based texturing technique is implemented for photorealistic reconstruction of the extracted planar surfaces. Experimental results using images collected by a camera mounted on a low-cost UAS demonstrate the feasibility of the proposed approach for photorealistic 3D surface reconstruction.

scholarly journals A 3D Surface Reconstruction Method for Large-Scale Point Cloud Data

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Baoyun Guo ◽  
Jiawen Wang ◽  
Xiaobin Jiang ◽  
Cailin Li ◽  
Benya Su ◽  
...  
Keyword(s):  
Parallel Computing ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Large Scale ◽  
Divide And Conquer ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Surface Reconstruction ◽  
3D Surface ◽  
Scale Point

Due to the memory limitation and lack of computing power of consumer level computers, there is a need for suitable methods to achieve 3D surface reconstruction of large-scale point cloud data. A method based on the idea of divide and conquer approaches is proposed. Firstly, the kd-tree index was created for the point cloud data. Then, the Delaunay triangulation algorithm of multicore parallel computing was used to construct the point cloud data in the leaf nodes. Finally, the complete 3D mesh model was realized by constrained Delaunay tetrahedralization based on piecewise linear system and graph cut. The proposed method performed surface reconstruction on the point cloud in the multicore parallel computing architecture, in which memory release and reallocation were implemented to reduce the memory occupation and improve the running efficiency while ensuring the quality of the triangular mesh. The proposed algorithm was compared with two classical surface reconstruction algorithms using multigroup point cloud data, and the applicability experiment of the algorithm was carried out; the results verify the effectiveness and practicability of the proposed approach.

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