Stanford researchers have created the first large-scale dataset of aerial videos from multiple classes of targets interacting in complex outdoor spaces.
Stanford inventors have developed a deep learning framework that is able to label individual points from 3D Point Clouds that are acquired by various sensors (RGBD sensors, LIDAR sensors, etc.). This framework obtains a point-level fine-grained labeling of 3D Scenes.
Stanford researchers have developed a method that allows for 3D semantic parsing of indoor spaces. It receives a 3D point cloud input which is parsed into individual spaces and specific components, such as structural and furniture.