| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes
|
Abu Alhaija, Hassan
|
|
2018
|
126 |
9 |
p. 961-972
|
artikel
|
| 2 |
Configurable 3D Scene Synthesis and 2D Image Rendering with Per-pixel Ground Truth Using Stochastic Grammars
|
Jiang, Chenfanfu
|
|
2018
|
126 |
9 |
p. 920-941
|
artikel
|
| 3 |
3D Interpreter Networks for Viewer-Centered Wireframe Modeling
|
Wu, Jiajun
|
|
2018
|
126 |
9 |
p. 1009-1026
|
artikel
|
| 4 |
Image-Based Synthesis for Deep 3D Human Pose Estimation
|
Rogez, Grégory
|
|
2018
|
126 |
9 |
p. 993-1008
|
artikel
|
| 5 |
Semantic Foggy Scene Understanding with Synthetic Data
|
Sakaridis, Christos
|
|
2018
|
126 |
9 |
p. 973-992
|
artikel
|
| 6 |
Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications
|
Müller, Matthias
|
|
2018
|
126 |
9 |
p. 902-919
|
artikel
|
| 7 |
Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance
|
Hattori, Hironori
|
|
2018
|
126 |
9 |
p. 1027-1044
|
artikel
|
| 8 |
The Reasonable Effectiveness of Synthetic Visual Data
|
Gaidon, Adrien
|
|
2018
|
126 |
9 |
p. 899-901
|
artikel
|
| 9 |
Virtual Training for a Real Application: Accurate Object-Robot Relative Localization Without Calibration
|
Loing, Vianney
|
|
2018
|
126 |
9 |
p. 1045-1060
|
artikel
|
| 10 |
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
|
Mayer, Nikolaus
|
|
2018
|
126 |
9 |
p. 942-960
|
artikel
|
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