This directory showcases synthetic data generation using blendtorch for supervised machine learning. In particular, we use composite rendering to extract normals and depths from a randomized scene. The scene is composed of fixed plane and a number of parametric 3D supershapes. Using physics, we drop a random initial constellation of objects onto the plane. Once the object come to rest (we speed up the physics, so this roughly happens after a single frame), we publish dense camera depth and normal information.
This sample uses the compositor to access different render passes. Unfortunately, Blender (2.9) does not offer a straight forward way to access the result of various render passes in memory. Therefore, btb.CompositeRenderer requires FileOutput nodes for temporary storage of data. For this purpose a fast OpenEXR reader, py-minexr was developed and integrated into blendtorch.
Camera normals are generated by a custom geometry-based material. Since colors must be in range (0,1), but normals are in (-1,1) a transformation is applied to make them compatible with color ranges. Hence, in PyTorch apply the following transformation to get true normals
true_normals = (normals - 0.5)*np.array([2., 2., -2.]).reshape(1,1,1,-1) # BxHxWx3To recreate these results run generate.py
python generate.py