Findeton / deepview

An implementation of Google's Deepview paper https://augmentedperception.github.io/deepview/

Deepview: View Synthesis with Learned Gradient Descent

An amateur implementation of Google's Deepview paper on View Synthesis with Multiplane Images (MPI) https://augmentedperception.github.io/deepview/

Results

An MPI generated with this code with a trained model, at the moment, looks like this:

Train

You can also run the following code in this Colab notebook. I've also included a copy of the notebook in the repo and I've published a blogpost as well.

We'll use the Spaces and the Real Estate 10K datasets to train the model. I'll use the notebook format here so if the line starts with a ! bang it's bash and otherwise it's Python code.

Let's start with the spaces dataset:

! git clone https://github.com/Findeton/deepview.git
! cd deepview && git checkout . && git checkout main && git pull
! git clone https://github.com/augmentedperception/spaces_dataset.git
! cd deepview && pip3 install -r requirements.txt
! mkdir -p deepview/trained-models
for i in range(10):
   !  cd deepview && DSET_NAME=spaces:1deterministic SPACES_PATH=/content/spaces_dataset/ python3 train.py

Now let's create an MPI:

! cd deepview && DSET_NAME=spaces:1deterministic SCENE_INDEX=1 SPACES_PATH=/content/spaces_dataset python3 tiled_render_spaces.py
import IPython
IPython.display.HTML(filename='/content/deepview/generated-html/deepview-mpi-viewer.html')

Let's use a reduced set of the Real State 10K dataset to further train it:

! git clone https://gitlab.com/Findeton/real-estate-10k-run0.git
for i in range(10):
! cd deepview && DSET_NAME=re:1random RE_PATH=/content/real-estate-10k-run0/ python3 train.py

Let's show an MPI from that dataset:

! cd deepview && DSET_NAME=re:1random TILE_W=200 TILE_H=200 SCENE_INDEX=1 RE_PATH=/content/real-estate-10k-run0 python3 tiled_render_spaces.py
import IPython
IPython.display.HTML(filename='/content/deepview/generated-html/deepview-mpi-viewer.html')

You can use the full Real Estate 10K dataset to train the model, but it's large and requires pre-processing so I've split it into 39 repos! The first one is the following and you only have to replace the last number to get the rest:

https://gitlab.com/Findeton/real-estate-10g-1