Body scanning oriented 3D object modelling method based on multi-view depth camera

2019 ◽  
Vol 11 (2) ◽  
pp. 84
Author(s):  
Ping Xu ◽  
Fengjun Hu ◽  
Ji Gao ◽  
Jianwei Lin ◽  
Huafeng Chen
Keyword(s):  
Depth Camera ◽  
3D Object ◽  
Body Scanning ◽  
Object Modelling ◽  
Modelling Method

ICOME: An Immersive Collaborative 3D Object Modelling Environment

1999 ◽  
Vol 4 (2) ◽  
pp. 129-138 ◽  
Author(s):  
C. Raymaekers ◽  
T. De Weyer ◽  
K. Coninx ◽  
F. Van Reeth ◽  
E. Flerackers
Keyword(s):  
3D Object ◽  
Object Modelling ◽  
Modelling Environment

scholarly journals Digital hologram generation for a real 3D object using by a depth camera

2013 ◽  
Vol 415 ◽  
pp. 012049 ◽  
Author(s):  
Seung Hyun Lee ◽  
Soon Chul Kwon ◽  
Ho Byung Chae ◽  
Ji Yong Park ◽  
Hoon Jong Kang ◽  
...  
Keyword(s):  
Depth Camera ◽  
3D Object ◽  
Digital Hologram

scholarly journals The possibilities of using drones in the 3D object modelling field

2018 ◽  
Vol 210 ◽  
pp. 04051 ◽  
Author(s):  
Milan Adamek ◽  
Vaclav Mach ◽  
Petr Neumann
Keyword(s):  
Mobile Applications ◽  
3D Modelling ◽  
3D Object ◽  
Security Technologies ◽  
Separate Part ◽  
Object Modelling ◽  
Modelling Process

This article is focused on the 3D Modelling of Objects from recordings taken by drones. The principles of multi-propeller devices and individual commercially-available parts for the construction of drones. A separate part is devoted to the Photogrammetry technique for the 3D modelling of objects from images taken by drones. It maps the currently available software for this method, and presents tools available online, as well as PC tools and mobile applications. The practical part describes the construction and assembly of one’s own multi-propeller machine and its subsequent use for collecting imagery material for the modelling of selected objects. The modelling process – with the support of selected software, is also described. The result is evaluated from the perspective of its possible use in Safety/Security Technologies.

scholarly journals Realworld 3D Object Recognition Using a 3D Extension of the HOG Descriptor and a Depth Camera

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 910
Author(s):  
Cristian Vilar ◽  
Silvia Krug ◽  
Mattias O’Nils
Keyword(s):  
Object Recognition ◽  
Autonomous Vehicles ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Real Data ◽  
3D Object Recognition ◽  
Depth Camera ◽  
3D Object ◽  
Real Objects ◽  
Histogram Of Gradients

3D object recognition is an generic task in robotics and autonomous vehicles. In this paper, we propose a 3D object recognition approach using a 3D extension of the histogram-of-gradients object descriptor with data captured with a depth camera. The presented method makes use of synthetic objects for training the object classifier, and classify real objects captured by the depth camera. The preprocessing methods include operations to achieve rotational invariance as well as to maximize the recognition accuracy while reducing the feature dimensionality at the same time. By studying different preprocessing options, we show challenges that need to be addressed when moving from synthetic to real data. The recognition performance was evaluated with a real dataset captured by a depth camera and the results show a maximum recognition accuracy of 81.5%.

ICOME: An Immersive Collaborative 3D Object Modelling Environment

1999 ◽  
Vol 4 (4) ◽  
pp. 265-274 ◽  
Author(s):  
C. Raymaekers ◽  
T. De Weyer ◽  
K. Coninx ◽  
F. Van Reeth ◽  
E. Flerackers
Keyword(s):  
3D Object ◽  
Object Modelling ◽  
Modelling Environment
Sign in / Sign up

Export Citation Format

Share Document