DeepView

View Synthesis with Learned Gradient Descent

John Flynn jflynn@google.com

Michael Broxton broxton@google.com

Paul Debevec debevec@google.com

Matthew DuVall matthewduvall@google.com

Graham Fyffe fyffe@google.com

Ryan Overbeck rover@google.com

Noah Snavely snavely@google.com

Richard Tucker richardt@google.com

Google Inc. Click to view the paper.

Technical Video

Video with synthesized fly-throughs and depth visualizations of the scenes shown in the paper. Video with synthesized fly-throughs and depth visualizations of the scenes shown in the paper.

Example MPIs in our interactive viewer We present several scenes in an interactive viewer.

Note that these were made with a 16-view version of the model in the paper with a sparsity penalty to reduce unneeded content on occluded layers.

The Chrome browser is recommended. We present several scenes in an interactive viewer.Note that these were made with a 16-view version of the model in the paper with a sparsity penalty to reduce unneeded content on occluded layers.The Chrome browser is recommended.

Scene 9

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Scene 10

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Scene 23

low | high Scene 23

Scene 52

low | high Scene 52

Scene 56

low | high Scene 56

Scene 63

low | high Scene 63 Here are brief instructions for using the viewer.

Comparison with Soft3D and Zhou et al. on Spaces dataset

View a comparison on test scenes for 4-view (large baseline) case.



Comparison with Soft3D on Kalantari et al. dataset

View a comparison on test scenes from Kalantari et al.

Other Studies Click to see results from





Extended Training Details Click to see





Spaces training data Click Spaces dataset used to train DeepView and a script to compute the evaluation in the paper.





Click to see results from ablation of gradient components , or from varying the number of LGD iterations. Click to see details of the methods used to reduce RAM during training and inference, as well as the training and loss hyperparameters.Click here to access thedataset used to train DeepView and a script to compute the evaluation in the paper.