In this directory we provide rendering configuration template for mitsuba-shapenet to render different components of a ShapeNet model.
- template-i.xml configuration for rendering image.
- template-a.xml for albedo.
- template-s.xml for shading.
- template-r.xml for specular. We use homogeneous specular for the entire model.
- template-d.xml for depth, which is used to generate mask.
Note: In the experiments, we rendered albedo/shading/specular and then synthesized image by I=A*S+R. Depth is used to generate object mask.
It might be useful to look into albedo/depth configuration file if you want to render other 'field' in mitsuba, such as normal.
gen_script.py is used to generate rendering and synthesize scripts. Please set following environment:
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MITSUBA points to mitsuba renderer executable (e.g. mitsuba.exe in windows).
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SHAPENET_ROOT the directory contains extracted ShapeNet models.
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ENVMAP_ROOT the directory contains environment maps, with a 'list.txt' file. Each line of the list file contains an environment map filename.
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RENDER_ROOT the directory to put rendering scripts and results.
Recently ShapeNet released an official dataset separation. The script would automatically download the model list from ShapeNet, which contains models, categories, uuid and data separation. Then it would generate output directories for models under RENDER_ROOT, as well as two scripts: render.bat/render.sh and synthesize.bat/synthesize.sh.
- render.bat: render albedo/shading/specular/depth in HDR images.
- synthesize.bat: generate mask image from depth, convert HDR to LDR for albedo/shading/specular(for saving disk space), generate image by I=A*S+R. ImageMagick is required for image synthesizing.
Then, you can run these scripts under their directory. We strongly recommend to render on a cluster. Rendering for a single model under 92 environment maps takes about 45 min on an i7-2600 old PC.