Accuracy analysis of 3D object shape recovery using depth filtering algorithms

Author(s):  
Alexey Ruchay ◽  
Anastasia Kober ◽  
Vsevolod Kalschikov ◽  
Konstantin Dorofeev ◽  
Vladimir Kolpakov
Author(s):  
Kevin Karsch ◽  
Zicheng Liao ◽  
Jason Rock ◽  
Jonathan T. Barron ◽  
Derek Hoiem

Author(s):  
A N Ruchay ◽  
K A Dorofeev ◽  
V V Kalschikov

In this paper, we first analyze the accuracy of 3D object reconstruction using point cloud filtering applied on data from a RGB-D sensor. Point cloud filtering algorithms carry out upsampling for defective point cloud. Various methods of point cloud filtering are tested and compared with respect to the reconstruction accuracy using real data. In order to improve the accuracy of 3D object reconstruction, an efficient method of point cloud filtering is designed. The presented results show an improvement in the accuracy of 3D object reconstruction using the proposed point cloud filtering algorithm.


Author(s):  
Amaury Dame ◽  
Victor A. Prisacariu ◽  
Carl Y. Ren ◽  
Ian Reid

2013 ◽  
pp. 473-497
Author(s):  
Pavel Zemcik ◽  
Michal Spanel ◽  
Premysl Krsek ◽  
Miloslav Richter

This chapter contains an overview of methods for a 3D object shape from both the surface and the internal structure of the objects. The acquisition methods of interest are optical methods based on objects surface image processing and CT/NMR sensors that explore the object volume structure. The chapter also describes some methods for 3D shape processing. The focus is on 3D surface shape acquisition methods based on multiple views, methods using single view video sequences, and methods that use a single view with a controlled light source. In addition, the volume methods represented by CT/NMR are covered as well. A set of algorithms suitable for the acquired 3D data processing and simplification are shown to demonstrate how the models data can be processed. Finally, the chapter discusses future directions and then draws conclusions.


Author(s):  
Rabeeh Karimi Mahabadi ◽  
Christian Hane ◽  
Marc Pollefeys
Keyword(s):  

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