Advanced point cloud estimation based on multiple view geometry

2018 ◽  
Author(s):  
Jan Hlubik ◽  
Patrik Kamencay ◽  
Robert Hudec ◽  
Miroslav Benco ◽  
Peter Sykora
Keyword(s):  
Point Cloud ◽  
Multiple View Geometry ◽  
Multiple View

scholarly journals A square root unscented Kalman filter for multiple view geometry based stereo cameras/inertial navigation

2016 ◽  
Vol 13 (5) ◽  
pp. 172988141666485 ◽  
Author(s):  
Zhiwen Xian ◽  
Junxiang Lian ◽  
Mao Shan ◽  
Lilian Zhang ◽  
Xiaofeng He ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Inertial Navigation ◽  
Square Root ◽  
Multiple View Geometry ◽  
Multiple View ◽  
Stereo Cameras

scholarly journals Robust Fitting for Multiple View Geometry

2012 ◽  
pp. 738-751 ◽  
Author(s):  
Olof Enqvist ◽  
Erik Ask ◽  
Fredrik Kahl ◽  
Kalle Åström
Keyword(s):  
Multiple View Geometry ◽  
Robust Fitting ◽  
Multiple View

A Sequential Structure from Motion Algorithm for Static Scenes Based on VR-Simulation

2013 ◽  
Vol 712-715 ◽  
pp. 2389-2392
Author(s):  
Hao Peng Wang ◽  
Xiao Jing Li ◽  
Tong Pan ◽  
Kai Zhao
Keyword(s):  
Structure From Motion ◽  
Feature Tracking ◽  
Dynamic Scenes ◽  
Multiple View Geometry ◽  
The Core ◽  
Reconstruction Scheme ◽  
Multiple View ◽  
Sequential Structure ◽  
Algebraic Concepts ◽  
Motion Method

The paper presented a conventional sequential Structure from Motion method, introduced the algebraic concepts and the core techniques used effectively. It is especially essential to comprehend the static scene based sequential of Structure from Motion is extended to a simultaneous segmentation and reconstruction scheme for dynamic scenes. The first outcome is the multiple view geometry, the second is the feature tracking and geometry initializing, and the last is self-calibration.

3D Reconstruction Using Multiple View Stereo and a Brief Introduction to Kinect

2014 ◽  
pp. 15-37
Author(s):  
Brojeshwar Bhowmick
Keyword(s):  
3D Reconstruction ◽  
Projective Geometry ◽  
Point Cloud ◽  
Microsoft Kinect ◽  
Sparse Reconstruction ◽  
Basic Properties ◽  
Multiple View ◽  
The Subject ◽  
Dense Reconstruction ◽  
3D Information

This chapter deals with the methodology of 3D reconstruction, both sparse and dense. The basic properties of the projective geometry and the camera models are introduced to understand the preliminaries about the subject. A more detail can be found in the book (Hartley & Zisserman, 2000). The sparse reconstruction deals with reconstructing 3D points for few image points. There are gaps in the reconstructed 3D points. Dense reconstruction tries to fill up gaps and make the density of the reconstruction higher. Estimation of correspondences is an integral part of multiview reconstruction and the author will discuss the point correspondences among images here. Finally the author will introduce the Microsoft Kinect, a divice which directly capture 3D information in realtime, and will show how to enhance the Kinect point cloud using vision framework.

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