Deep Blending for Free-Viewpoint Image-Based-Rendering: Difference between revisions

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==Method==
==Method==
Below is the pipeline for their system
Below is the pipeline for their system. Note that they train the CNN separately than their proposed pipeline.


===Off-line Scene Processing===
===Off-line Scene Processing===
In this step, they perform the following:
In this step, they perform the following:
* Structure from Motion Registration to calibrate the cameras (i.e. get the extrinsics/pose)
* Structure from Motion<cite><ref name="schonberger2016sfm">Johannes L. Schönberger ; Jan-Michael Frahm, Structure-from-Motion Revisited (CVPR 2016) [https://doi.org/10.1109/CVPR.2016.445 DOI:10.1109/CVPR.2016.445]</ref></cite> Registration to calibrate the cameras (i.e. get the extrinsics/pose)  
*
* Multiview Stereo Reconstruction (MVS) to generate per-view depth maps and per-view meshes using two methods.
** COLMAP which provides fine details but a sparser reconstruction
** Delauney tetrahedralization (RealityCapture 2016) which provides more completeness and a smoother estimate
* Geometry Refinement
* Mesh Simplification
 
===Off-line CNN Training===
The goal of the CNN is to generate a sharp temporally consistent image by blending multiple estimates.
 
The CNN is trained via hold-out.
 
===On-line Pipeline===
* Given a novel viewpoint, create a voxel grid where each voxel contains indices of per-view mesh triangles.
* Generate a global mesh render from the novel viewpoint.
* Use InsideOut to create 4 ''mosaics'', warped input views.
* Input the global mesh render and mosaics into the deep blending CNN.
* Blend the mosaics and the global mesh render.


==Architecture==
==Architecture==
The architecture they use is a U-Net with a fixed set of inputs.
==Evaluation==
==Evaluation==
==Resources==
==Resources==
==References==
==References==