doc3D-renderer
Doc3D is the first 3D dataset focused on document unwarping with realistic paper warping and renderings.
This repository contains all the rendering codes of doc3D. The download scripts of the dataset is available in [doc3D-dataset](https://github.com/cvlab-stonybrook/doc3D-dataset) repo.Instructions to render your own data:
-
Step 1: We need a few assets to start the rendering.
- a) Meshes
- b) HDR environment maps for lighting
- c) Textures
For your convenience, we already provide some sample meshes (
/obj
), HDR maps (/env
) and textures (/tex
). Each line is a path to a mesh, HDR map and a texture.
-
Step 2: List the assets in
objs.csv
,envs.csv
, andtexs.csv
. -
Step 3a : Run the rendering code (for images, UVs, 3D coordinates):
blender --background --python render_mesh.py -- <folder-id> <start-mesh> <end-mesh>
This command renders the images (/img
), 3D coordinates (/wc
) and UV (/uv
) in folder<folder-id>
.<start-mesh>
and<end-mesh>
refers to line numbers inobjs.csv
specifying the meshes to be used while rendering. Additionally,render_mesh.py
also saves the Blender model (.blend
) files for further rendering process (albedo
,norm
etc.). Toggle it using thesave_blend_file=False
flag in the code.- For faster rendering you can use the multiprocessing code
batch_render.py
. Runpython batch_render.py <folder-id> <start-mesh> <end-mesh>
. Remember to modify the processor counts usingnproc
.
-
Step 3b : Run the rendering code (for checkerboards, albedos, depth etc.):
- Run
blendnames.py
to list the available.blend
files,python blendnames.py <folder-id>
. Remember to do this step! - Albedos (
/alb
):blender --background --python render_alb.py -- <folder-id> <start-mesh> <end-mesh>
- Normals (
/norm
):blender --background --python render_norm.py -- <folder-id> <start-mesh> <end-mesh>
- Depths (
/dmap
):blender --background --python render_dmap.py -- <folder-id> <start-mesh> <end-mesh>
- Checkerboard (
/recon
):blender --background --python render_recon.py -- <folder-id> <start-mesh> <end-mesh>
- Run
-
Step 4: If you want to create the backward mappings from UV:
/uv2backwardmap
contains the necessary scripts. We use MatLab to do this.python exr2mat.py <folder-id>
, converts the.exr
files to.mat
files.- Edit the
src_dir
anddst_dir
accordingly and runfm2bm.m
.
Citation:
If you use the dataset or this code, please consider citing our work-
@inproceedings{SagnikKeICCV2019,
Author = {Sagnik Das*, Ke Ma*, Zhixin Shu, Dimitris Samaras, Roy Shilkrot},
Booktitle = {Proceedings of International Conference on Computer Vision},
Title = {DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks},
Year = {2019}}
Acknowlegement:
Thanks to the awesome software, Blender, thanks to it's developers and also to the super awesome community.