Real-time depth map generation using hybrid multi-view cameras

2014 ◽  
Author(s):  
Yunseok Song ◽  
Dong-Won Shin ◽  
Eunsang Ko ◽  
Yo-Sung Ho
Keyword(s):  
Real Time ◽  
Depth Map ◽  
Map Generation

Hierarchical real-time depth map generation algorithm

2013 ◽  
Author(s):  
Istvan Andorko ◽  
Piotr Stec ◽  
Alexandru Drimbarean ◽  
Petronel Bigioi
Keyword(s):  
Real Time ◽  
Depth Map ◽  
Generation Algorithm ◽  
Map Generation

Real-time depth map generation architecture for 3D videoconferencing

2010 ◽  
Author(s):  
John Congote ◽  
Inigo Barandiaran ◽  
Javier Barandiaran ◽  
Tomas Montserrat ◽  
Julien Quelen ◽  
...  
Keyword(s):  
Real Time ◽  
Depth Map ◽  
Map Generation

scholarly journals Real-Time 2D-to-3D Conversion for 3DTV using Time-Coherent Depth-Map Generation Method

2014 ◽  
Vol 10 (3) ◽  
pp. 9-16
Author(s):  
Seung-Woo Nam ◽  
Hye-Sun Kim ◽  
Yun-Ji Ban ◽  
Sung-Il Chien
Keyword(s):  
Real Time ◽  
Depth Map ◽  
2D To 3D Conversion ◽  
Map Generation ◽  
2D To 3D

scholarly journals A Residual Network and FPGA Based Real-Time Depth Map Enhancement System

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 546
Author(s):  
Zhenni Li ◽  
Haoyi Sun ◽  
Yuliang Gao ◽  
Jiao Wang
Keyword(s):  
Real Time ◽  
Super Resolution ◽  
Depth Map ◽  
Acquisition System ◽  
Depth Image ◽  
Fpga Design ◽  
Depth Sensing ◽  
Residual Network ◽  
Real Time Processing ◽  
Depth Maps

Depth maps obtained through sensors are often unsatisfactory because of their low-resolution and noise interference. In this paper, we propose a real-time depth map enhancement system based on a residual network which uses dual channels to process depth maps and intensity maps respectively and cancels the preprocessing process, and the algorithm proposed can achieve real-time processing speed at more than 30 fps. Furthermore, the FPGA design and implementation for depth sensing is also introduced. In this FPGA design, intensity image and depth image are captured by the dual-camera synchronous acquisition system as the input of neural network. Experiments on various depth map restoration shows our algorithms has better performance than existing LRMC, DE-CNN and DDTF algorithms on standard datasets and has a better depth map super-resolution, and our FPGA completed the test of the system to ensure that the data throughput of the USB 3.0 interface of the acquisition system is stable at 226 Mbps, and support dual-camera to work at full speed, that is, 54 fps@ (1280 × 960 + 328 × 248 × 3).

scholarly journals Stochastic-Depth Ambient Occlusion

2021 ◽  
Vol 4 (1) ◽  
pp. 1-15
Author(s):  
Jop Vermeer ◽  
Leonardo Scandolo ◽  
Elmar Eisemann
Keyword(s):  
Real Time ◽  
High Performance ◽  
Depth Map ◽  
Visual Quality ◽  
Ambient Light ◽  
Missing Information ◽  
Time Performance ◽  
Ambient Occlusion ◽  
Depth Buffer ◽  
Screen Space

Ambient occlusion (AO) is a popular rendering technique that enhances depth perception and realism by darkening locations that are less exposed to ambient light (e.g., corners and creases). In real-time applications, screen-space variants, relying on the depth buffer, are used due to their high performance and good visual quality. However, these only take visible surfaces into account, resulting in inconsistencies, especially during motion. Stochastic-Depth Ambient Occlusion is a novel AO algorithm that accounts for occluded geometry by relying on a stochastic depth map, capturing multiple scene layers per pixel at random. Hereby, we efficiently gather missing information in order to improve upon the accuracy and spatial stability of conventional screen-space approximations, while maintaining real-time performance. Our approach integrates well into existing rendering pipelines and improves the robustness of many different AO techniques, including multi-view solutions.

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