Researchers at Stanford and Princeton universities and the Technical University of Munich (TUM) in Germany have created ScanNet, an immense new database of three-dimensional (3D) images with millions of annotated objects.
Their goal is for ScanNet to help train machines to better understand the physical world via deep learning.
The team used a 3D camera to scan 1,513 scenes and construct the dataset, while volunteers provided annotations via Amazon's Mechanical Turk platform.
The application of deep learning enabled ScanNet to reliably identify many objects using only their shape or depth information, says TUM professor Matthias Niessner.
Carnegie Mellon University professor Siddhartha Srinivas says the new dataset could be a "good start" toward enabling machines to understand the interiors of homes. "Although simulating real-life imagery is often unrealistic, as you can see from the [computer-generated imagery] in movies, simulating depth is quite realistic," he says.
From Technology Review
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