Blender for Computer Vision Machine Learning

The Pixelary was approached to assist in a computer learning project that required thousands of bathroom images to train autonomous cleaning robots, and Blender was used to generate photo-realistic imagery.

Mike Pan writes:

Rather than collecting real photos and tagging them manually, we decided it would be faster to generate all the images with CGI, and then feed those images back into the computer. This approach has a few distinct advantages over crowd-sourced photo labelling:

• We can generate pixel-perfect masks and depth data for the ML system automatically.
• We can cover a comprehensive range of washroom design, layout, lighting and colors.
• The image generation system we build can be used for other things in the future.

So with these goals in mind, we set out to create a 3D washroom in Blender. Our virtual robotics training scene consists of 40+ real toilet models and layouts. We even included sinks and tubs to make sure that the ML algorithm doesn’t overfit the data and think everything that’s shiny and can hold water is a toilet bowl.