Research on the Self-Similarity of Point Cloud Outline for Accurate Compression

Point cloud has been used for industrial modeling for several years. It is created for industries purpose such as point cloud reconstruction, robot recognition, etc. Because Point Cloud Data need a lot of storage space. Especially in the application of large-scale scene reconstruction and cultural r...

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Bibliographic Details
Published in:2015 International Conference on Smart and Sustainable City and Big Data (ICSSC) p. 5
Main Authors: An, Xuandong, Yu, Xiaoging, Zhang, Yifan
Format: Conference Proceeding
Language:English
Published: Stevenage, UK IET 2015
The Institution of Engineering & Technology
Subjects:
Algorithms
Bandwidths
Clustering
Computer vision and image processing techniques
Data compression
Data transmission
Image and video coding
Image recognition
Image reconstruction
Image segmentation
Lasers
Rapid prototyping
Reconstruction
Redundancy
Rendering
Reverse engineering
Scale (ratio)
Scanning
Segmentation
Self-similarity
Similarity
Three dimensional models
data compression
point cloud outline self-similarity
industrial modeling
coding methods
RoPS algorithm
point cloud data compression
feature extraction
image segmentation
storage space
point cloud data transmission
cultural relics preservation
point cloud area segmentation
large-scale scanning point cloud model
image coding
large-scale scene reconstruction
Qsplat method
ISBN:9781785610325, 1785610325
Online Access:Get full text
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Summary:Point cloud has been used for industrial modeling for several years. It is created for industries purpose such as point cloud reconstruction, robot recognition, etc. Because Point Cloud Data need a lot of storage space. Especially in the application of large-scale scene reconstruction and cultural relics preservation. As Point Cloud Data compression has become an important subject. In order to process large amount of Point Cloud Data. It must guarantee a lot of point cloud data transmission in a limited bandwidth and restore the original information. The most effective way is to use the coding methods. This paper presents a novel method to compression large-scale scanning point cloud model. By calculating the feature of a point cloud model, we can find the similarity of the point cloud data and use code to replace the point which has the same feature. Qsplat method is being used to rendering and segmentation point cloud area into some similar block, and the latest RoPS algorithm is proposed to statistic the similar features of each block. By clustering block with same feature and using the block which has the similar feature to replace each-other. We can achieve the requirement of point cloud compression by remove the redundancy and recover a satisfy Point Could Data.
Bibliography:ObjectType-Conference Proceeding-1
SourceType-Conference Papers & Proceedings-1
content type line 21
ISBN:9781785610325
1785610325
DOI:10.1049/cp.2015.0272