An improved enhancement layer for octree based point cloud compression with plane projection approximation

2016 ◽  
Author(s):  
Khartik Ainala ◽  
Rufael N. Mekuria ◽  
Birendra Khathariya ◽  
Zhu Li ◽  
Ye-Kui Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Enhancement Layer ◽  
Projection Approximation ◽  
Plane Projection

scholarly journals Neural Point Cloud Rendering via Multi-Plane Projection

2020 ◽  
Author(s):  
Peng Dai ◽  
Yinda Zhang ◽  
Zhuwen Li ◽  
Shuaicheng Liu ◽  
Bing Zeng
Keyword(s):  
Point Cloud ◽  
Plane Projection

scholarly journals Optimization Algorithm for Point Cloud Quality Enhancement Based on Statistical Filtering

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qian Zhao ◽  
Xiaorong Gao ◽  
Jinlong Li ◽  
Lin Luo
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
External Environment ◽  
Target Point ◽  
Quality Enhancement ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Plane Projection ◽  
Enhancement Algorithm

In the process of acquiring point cloud data by a 3D laser scanner, some problems, such as outliers, mixed points, and holes, may be caused in the target point cloud due to the external environment, the discreteness of the laser beam, and the occlusion of objects. In this paper, a point cloud quality optimization and enhancement algorithm is designed. A self-adaptive octree is established to rasterize the point cloud and calculate the density of each grid, combing with the statistical filtering to remove outliers from the point cloud data. Then, a plane projection method is used for removing the confounding points from the point cloud data. Finally, the point cloud is triangulated and a priority value is set, and then, points are preferentially inserted where the priority value is the largest to repair the holes. Experiments show that while removing outliers and confounding points, the detailed features of the point cloud can be maintained, holes are effectively filled, and the quality of the point cloud is effectively improved.

A Proposal of 3D Feature based on Occupancy of Point Cloud in Multi-Scale Shell Region

2016 ◽  
Vol 136 (8) ◽  
pp. 1078-1084
Author(s):  
Shoichi Takei ◽  
Shuichi Akizuki ◽  
Manabu Hashimoto
Keyword(s):  
Point Cloud ◽  
Multi Scale ◽  
Feature Based ◽  
Shell Region

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

Hierarchical point cloud denoising algorithm

2020 ◽  
Vol 28 (7) ◽  
pp. 1618-1625
Author(s):  
Fu-qun ZHAO ◽  
◽  
Keyword(s):  
Point Cloud
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