On the view synthesis of man-made scenes using uncalibrated cameras

2007 ◽  
Vol 28 (3) ◽  
pp. 384-393 ◽  
Author(s):  
Geetika Sharma ◽  
Santanu Chaudhury ◽  
J.B. Srivastava
Keyword(s):  
View Synthesis ◽  
Uncalibrated Cameras

View-synthesis from uncalibrated cameras and parallel planes

2019 ◽  
Vol 79 ◽  
pp. 40-53
Author(s):  
Antonio Canclini ◽  
Francesco Malapelle ◽  
Marco Marcon ◽  
Stefano Tubaro ◽  
Andrea Fusiello
Keyword(s):  
View Synthesis ◽  
Uncalibrated Cameras

Object-centered View Synthesis using Learning-based Image Inpainting

2019 ◽  
pp. 154
Author(s):  
HONG-CHANG SHIN ◽  
Gwangsoon Lee ◽  
Ho min Eum ◽  
Jeong-Il Seo
Keyword(s):  
Image Inpainting ◽  
View Synthesis

Passthrough+

2020 ◽  
Vol 3 (1) ◽  
pp. 1-17
Author(s):  
Gaurav Chaurasia ◽  
Arthur Nieuwoudt ◽  
Alexandru-Eugen Ichim ◽  
Richard Szeliski ◽  
Alexander Sorkine-Hornung
Keyword(s):  
Real Time ◽  
Video Encoding ◽  
View Synthesis ◽  
Performance Constraints ◽  
Trade Offs ◽  
Stereoscopic View ◽  
Target User ◽  
Spatio Temporal ◽  
And Performance ◽  
3D Scene

We present an end-to-end system for real-time environment capture, 3D reconstruction, and stereoscopic view synthesis on a mobile VR headset. Our solution allows the user to use the cameras on their VR headset as their eyes to see and interact with the real world while still wearing their headset, a feature often referred to as Passthrough. The central challenge when building such a system is the choice and implementation of algorithms under the strict compute, power, and performance constraints imposed by the target user experience and mobile platform. A key contribution of this paper is a complete description of a corresponding system that performs temporally stable passthrough rendering at 72 Hz with only 200 mW power consumption on a mobile Snapdragon 835 platform. Our algorithmic contributions for enabling this performance include the computation of a coarse 3D scene proxy on the embedded video encoding hardware, followed by a depth densification and filtering step, and finally stereoscopic texturing and spatio-temporal up-sampling. We provide a detailed discussion and evaluation of the challenges we encountered, as well as algorithm and performance trade-offs in terms of compute and resulting passthrough quality.;AB@The described system is available to users as the Passthrough+ feature on Oculus Quest. We believe that by publishing the underlying system and methods, we provide valuable insights to the community on how to design and implement real-time environment sensing and rendering on heavily resource constrained hardware.

Unsupervised Stereoscopic Image Retargeting via View Synthesis and Stereo Cycle Consistency Losses

2021 ◽  
Author(s):  
Xiaoting Fan ◽  
Jianjun Lei ◽  
Jie Liang ◽  
Yuming Fang ◽  
Xiaochun Cao ◽  
...  
Keyword(s):  
View Synthesis ◽  
Stereoscopic Image ◽  
Image Retargeting

Disocclusion-type aware hole filling method for view synthesis

2021 ◽  
Author(s):  
Xiaodong Chen ◽  
Haitao Liang ◽  
Huaiyuan Xu ◽  
Siyu Ren ◽  
Huaiyu Cai ◽  
...  
Keyword(s):  
View Synthesis ◽  
Hole Filling ◽  
Filling Method
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