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Mapillary is a company that processes high volumes of images to develop a labeled 3-D model of the physical world. Mapillary’s APIs allow developers to build applications that are aware of stop signs, buildings, streets, trees, and other physical objects in real-world space. The potential use cases for Mapillary are numerous, ranging from self-driving cars to augmented reality.
We can now build a 3-D model of the real world. It’s not a perfect representation of reality, but it is much better than we had just a few years ago. What has changed? How have the tools advanced such that we are able to build an API for accessing accurate information about the physical world around us?
Mapillary is possible because of a combination of modern developments.
High quality smartphone cameras enable users to crowdsource images of the world around them. Cloud computing allows for cheap workload processing. Newer computer vision techniques allow 2-D images to be stitched together in a 3-D representation. Deep learning architectures improve the classification and segmentation of objects in an image.
Peter Kontschieder is the head of research at Mapillary, and he joins the show to talk about the technologies and research that has enabled Mapillary to build a futuristic business–an API for accessing information about the physical world.
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