Automatic dense visual semantic mapping from street-level imagery

This paper describes a method for producing a semantic map from multi-view street-level imagery. We define a semantic map as an overhead, or bird's eye view of a region with associated semantic object labels, such as car, road and pavement. We formulate the problem using two conditional random fields. The first is used to model the semantic image segmentation of the street view imagery treating each image independently. The outputs of this stage are then aggregated over many images to form the input for our semantic map that is a second random field defined over a ground plane. Each image is related by a simple, yet effective, geometrical function that back projects a region from the street view image into the overhead ground plane map. We introduce, and make publicly available, a new dataset created from real world data. Our qualitative evaluation is performed on this data consisting of a 14. 8 km track, and we also quantify our results on a representative subset.

Authors

• Sunando Sengupta
• Paul Sturgess
• Lubor Ladicky
• Philip H. S. Torr

Keywords

• Semantics
• Cameras
• Image segmentation
• Vehicles
• Labeling
• Visualization
• Roads
• Density Map
• Visual Map
• Semantic Map
• Street-level Imagery
• Random Fields
• Real-world Data
• Semantic Segmentation
• Ground Plane
• Street View
• Bird’s Eye
• Conditional Random Field
• Output Stage
• View Of Region
• Street View Images
• Computer Vision
• Shadowing Effect
• Street Level
• Homography
• Mapping Problem
• Conditional Random Field Model
• Camera Height