Awesome

Overcoming Occlusion with Inverse Graphics

This is the code for our ECCV16 paper on the Geometry meets Deep Learning workshop.

Here you'll find the scene generator as well as the code for the models. The main libraries needed are:

• My updated versions of OpenDR and Chumpy you can find here.
• Blender 2.6+ (installed as a python module), so you can do "import bpy". Please follow these instructions.
• OpenCV 3.0+ as Python module.

Python 3.4+ is a requirement.

The main script files to run are the following:

• diffrender_demo.py: Code to interactively run and fit the generative models.
• diffrender_groundtruth.py: Main code to generate ground-truth for synthetic scenes (both for OpenGL and Photorealistic (cycles) types of rendering!
• diffrender_groundtruth_multi.p: As above, but extended to multiple objects.
• diffrender_experiment.py: Generate experiment train/test splits.
• diffrender_train.py: Train Lasagne neural networks (used to train our recognition models).
• diffrender_test.py: Main code to evaluate and fit different models.
• diffrender_analyze.py: Extract statistics and plots of experimental evaluation.

For the stocastic generation of synthetic images with ground-truth, you'll need additional CAD data and other files. Please get in touch with me (polmorenoc@gmail.com) for it.