scholarly journals Classification and Pose Estimation of Vehicles in Videos by 3D Modeling within Discrete-Continuous Optimization

2012 ◽  
Author(s):  
Michael Hodlmoser ◽  
Branislav Micusik ◽  
Ming-Yu Liu ◽  
Marc Pollefeys ◽  
Martin Kampel
Keyword(s):  
Pose Estimation ◽  
3D Modeling ◽  
Continuous Optimization

Pose estimation and 3D modeling from video by volume feedback

2003 ◽  
Author(s):  
Alireza Nasiri Avanaki ◽  
Babak Hamidzadeh ◽  
Faouzi Kossentini
Keyword(s):  
Pose Estimation ◽  
3D Modeling

A Model Is Worth 1,000 Pictures: Applications of 3D-Modeling in Skull Base Surgery Neuroanatomy Education

2020 ◽  
Author(s):  
Christopher S. Graffeo ◽  
Avital Perry ◽  
Lucas P. Carlstrom ◽  
Michael J. Link ◽  
Jonathan Morris
Keyword(s):  
Skull Base ◽  
3D Modeling ◽  
Skull Base Surgery

3D modeling of the vascular system

2016 ◽  
Vol 1 (1) ◽  
pp. 51-58 ◽  
Author(s):  
Jean François Uhl ◽  
Maxime Chahim ◽  
François Cros ◽  
Amina Ouchene ◽  
◽  
...  
Keyword(s):  
Augmented Reality ◽  
3D Modeling ◽  
Vascular System ◽  
Arterial System ◽  
Chronic Venous Disease ◽  
Venous Disease ◽  
Software Products ◽  
Operative Planning ◽  
Morphological Modeling ◽  
Pre Treatment

The 3D modeling of the vascular system could be achieved in different ways: In the venous location, the morphological modeling by MSCT venography is used to image the venous system: this morphological modeling tool accurately investigates the 3D morphology of the venous network of our patients with chronic venous disease. It is also a fine educational tool for students who learn venous anatomy, the most complex of the human body. Another kind of modeling (mathematical modeling) is used to simulate the venous functions, and virtually tests the efficacy of any proposed treatments. To image the arterial system, the aim of 3D modeling is to precisely assess and quantify the arterial morphology. The use of augmented reality before an endovascular procedure allows pre-treatment simulation, assisting in pre-operative planning as well as surgical training. In the special field of liver surgery, several 3D modeling software products are available for computer simulations and training purposes and augmented reality.

3D Object Recognition and Relative Localization using a 3D sensor Embedded on a Mobile Robot

2020 ◽  
Author(s):  
Gopi Krishna Erabati
Keyword(s):  
Pose Estimation ◽  
Autonomous Systems ◽  
Microsoft Kinect ◽  
Object Localization ◽  
Initial Alignment ◽  
Relative Localization ◽  
3D Data ◽  
Industry Automation ◽  
Ofthe Object ◽  
3D Descriptors

The technology in current research scenario is marching towards automation forhigher productivity with accurate and precise product development. Vision andRobotics are domains which work to create autonomous systems and are the keytechnology in quest for mass productivity. The automation in an industry canbe achieved by detecting interactive objects and estimating the pose to manipulatethem. Therefore the object localization ( i.e., pose) includes position andorientation of object, has profound ?significance. The application of object poseestimation varies from industry automation to entertainment industry and fromhealth care to surveillance. The objective of pose estimation of objects is verysigni?cant in many cases, like in order for the robots to manipulate the objects,for accurate rendering of Augmented Reality (AR) among others.This thesis tries to solve the issue of object pose estimation using 3D dataof scene acquired from 3D sensors (e.g. Kinect, Orbec Astra Pro among others).The 3D data has an advantage of independence from object texture and invarianceto illumination. The proposal is divided into two phases : An o?ine phasewhere the 3D model template of the object ( for estimation of pose) is built usingIterative Closest Point (ICP) algorithm. And an online phase where the pose ofthe object is estimated by aligning the scene to the model using ICP, providedwith an initial alignment using 3D descriptors (like Fast Point Feature Transform(FPFH)).The approach we develop is to be integrated on two di?erent platforms :1)Humanoid robot `Pyrene' which has Orbec Astra Pro 3D sensor for data acquisition,and 2)Unmanned Aerial Vehicle (UAV) which has Intel Realsense Euclidon it. The datasets of objects (like electric drill, brick, a small cylinder, cake box)are acquired using Microsoft Kinect, Orbec Astra Pro and Intel RealSense Euclidsensors to test the performance of this technique. The objects which are used totest this approach are the ones which are used by robot. This technique is testedin two scenarios, fi?rstly, when the object is on the table and secondly when theobject is held in hand by a person. The range of objects from the sensor is 0.6to 1.6m. This technique could handle occlusions of the object by hand (when wehold the object), as ICP can work even if partial object is visible in the scene.

Methodology for 3D modeling of a graphite deposit based on reevaluation of archive data

2019 ◽  
Author(s):  
Jan Jelínek ◽  
František Staněk ◽  
Radomír Grygar ◽  
Jan Franěk ◽  
Michal Poňavič
Keyword(s):  
3D Modeling ◽  
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