Reconstruction of 3D Object Models from Vectorised Engineering Drawings

2002 ◽  
Vol 5 (1) ◽  
pp. 2-14 ◽  
Author(s):  
S. Ablameyko ◽  
V. Bereishik ◽  
A. Gorelik ◽  
S. Medvedev
Keyword(s):  
3D Object ◽  
Engineering Drawings ◽  
Object Models

scholarly journals Reconstruction and Verification of 3D Object Models for Grasping

2011 ◽  
pp. 315-328 ◽  
Author(s):  
Zoltan-Csaba Marton ◽  
Lucian Goron ◽  
Radu Bogdan Rusu ◽  
Michel Beetz
Keyword(s):  
3D Object ◽  
Object Models

A Recognition Method of Lung Nodule Shadows in X-Ray CT Images Using 3D Object Models

2003 ◽  
Vol 03 (04) ◽  
pp. 533-545 ◽  
Author(s):  
Hotaka Takizawa ◽  
Kanae Shigemoto ◽  
Shinji Yamamoto ◽  
Tohru Matsumoto ◽  
Yukio Tateno ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Lung Nodule ◽  
Ct Images ◽  
Recognition Method ◽  
X Ray ◽  
3D Object ◽  
3 Dimensional ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Object Models

In this paper, we describe a recognition method of lung nodule shadows in X-ray CT images using 3-dimensional nodule and blood vessel models. From these 3D object models, artificial CT images are generated as templates. The templates are then applied to input images which comprise of suspicious shadows. If any parameters of the suspicious shadow matches a nodule template rather than any blood vessel template, then it is determined to be abnormal. Otherwise, it is determined to be normal. By applying our new method to the actual lung CT images of 38 patients, the false positive ratio is reduced to 4.31 [shadow/patient] with the sensitivity exceeding 95%.

Autonomous Visualization of Real Environments for Telepresence Applications

1997 ◽  
Vol 6 (6) ◽  
pp. 645-657 ◽  
Author(s):  
Robert Geist ◽  
Todd Stinson ◽  
Robert Schalkoff ◽  
Sabri Gurbuz
Keyword(s):  
Virtual Environments ◽  
Structured Light ◽  
Surface Patch ◽  
Future Research ◽  
3D Object ◽  
Unstructured Environments ◽  
Direct Estimation ◽  
Active Laser ◽  
3D Surface ◽  
Object Models

The autonomous, noncontact creation of virtual environments from existing, real environments is described. The technique uses structured light to provide direct estimation of 3D surface patch parameters. Active (laser) cameras are used to determine 3D object models, and passive camera images are used to generate color and texture. This process, termed virtualization, has immediate application to providing telepresence in previously unmodeled or unstructured environments. An example of the process is shown and directions for future research are indicated.

Extra FAT: A Photorealistic Dataset for 6D Object Pose Estimation

2020 ◽  
Vol 2020 (8) ◽  
pp. 221-1-221-7
Author(s):  
Jianhang Chen ◽  
Daniel Mas Montserrat ◽  
Qian Lin ◽  
Edward J. Delp ◽  
Jan P. Allebach
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Object Segmentation ◽  
3D Object ◽  
Virtual Camera ◽  
Image Dataset ◽  
Rgb Images ◽  
Object Pose Estimation ◽  
Object Models

We introduce a new image dataset for object detection and 6D pose estimation, named Extra FAT. The dataset consists of 825K photorealistic RGB images with annotations of groundtruth location and rotation for both the virtual camera and the objects. A registered pixel-level object segmentation mask is also provided for object detection and segmentation tasks. The dataset includes 110 different 3D object models. The object models were rendered in five scenes with diverse illumination, reflection, and occlusion conditions.

Active 3D object models

2002 ◽  
Author(s):  
R.M. Bolle ◽  
A. Califano ◽  
R. Kjeldsen ◽  
R. Mohan
Keyword(s):  
3D Object ◽  
Object Models

scholarly journals Understanding Bayesian Rooms Using Composite 3D Object Models

2013 ◽  
Author(s):  
Luca Del Pero ◽  
Joshua Bowdish ◽  
Bonnie Kermgard ◽  
Emily Hartley ◽  
Kobus Barnard
Keyword(s):  
3D Object ◽  
Object Models

FREE-FORM 3D OBJECT RECOGNITION IN RANGE DATA USING WEAK CORRESPONDENCE BETWEEN LOCAL FEATURES

2003 ◽  
Vol 17 (07) ◽  
pp. 1245-1277
Author(s):  
RICHARD J. CAMPBELL ◽  
PATRICK J. FLYNN
Keyword(s):  
Object Recognition ◽  
Free Form ◽  
3D Object Recognition ◽  
Range Data ◽  
Dense Range ◽  
3D Object ◽  
Number Of Factors ◽  
Potential Applications ◽  
Recognition Systems ◽  
Object Models

Model-Based 3D object recognition systems have a variety of potential applications, but widespread use of such systems has not occurred, due to a number of factors including the representational limitations of models. One historical limitation is the discriminatory representation of free-form objects. The system described in this paper recognizes free-form objects in dense range data acquired by a structured light rangefinder. Images and object models are represented as a network of salient segments which are then brought into correspondence until a reliable pose estimate is available. Experiments with a database of images and object models highlight the contributions of this system.

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