Readit NewsOne way we're applying this is automatic creation of panoramic tours. Real estate is a big market for us, and a key differentiator of our product is the ability to create a tour of a home that will play automatically as either a slideshow or a 3D fly-through. The problem is, creating these tours manually takes time, as it requires navigating a 3D model to find the best views of each room. We know these tours add significant value when selling a home, but many of our customers don't have the time to create them. In our research lab we're using deep learning to create tours automatically by identifying different rooms of the house and what views of them tend to be appealing. We are drawing from a training set of roughly a million user-generated views from manually created guided tours, a decent portion of which are labelled with room type.
It's less far along, but we're also looking at semantic segmentation for 3D geometry estimation, deep learning for improved depth data quality, and other applications of deep learning to 3D data. Our customers have scanned about 370,000 buildings, which works out to around 300 million RGBD images of real places.