Next-generation, location-based computer vision (CV) applications like augmented reality or autonomous driving require robustly working CV algorithms. Robustness means that algorithms can cope with variability in input data like seasonal and weather-related appearance changes, low-quality data from cheap cameras, or data captured under suboptimal lighting conditions. Producing reliable predictions in such challenging data scenarios and for highly varying, city-scale environments makes a real impact for downstream applications.
To make this impact measurable, we are introducing a novel, city-scale data set called Mapillary Metropolis, available from August 31, 2021. This data set is designed with the goal of creating a completely novel and complex benchmarking paradigm for training and testing computer vision algorithms in the context of semantic 3D map making. Our new data set comprises multiple data modalities at a city-scale size, registered across different representations, and enriched with human- and machine-generated annotations for different object recognition and tracking tasks. These modalities include professional- and consumer-grade street-level images, aerial images, 3D point clouds from street-level LiDAR, aerial LiDAR, and image-based reconstruction (SfM and MVS), and CAD models. All data modalities are aligned based on manual correspondence annotations and ingestion of survey-grade ground control point data. Our data set is designed to take city-scale 3D semantic modeling to the next level by enabling researchers to study shortcomings in current methods including, but not limited to, object recognition and tracking, 3D modeling, depth estimation, relocalization, image retrieval, change detection, sensor-fusion, and so on.
We are soliciting high-quality, aspiring proposals leveraging our novel Metropolis data set to improve basic CV algorithms that use one or preferably multiple data modalities from our data set for improving semantic 3D building.
To foster further innovation in this area, and to deepen our collaboration with academia, Facebook is pleased to invite faculty to respond to this call for research proposals pertaining to the aforementioned topics. We anticipate issuing up to a total of five awards, each in the $50,000 to $100,000 range. Payment will be made to the proposer’s host university as an unrestricted gift, and researchers will be invited to join subsequent workshops and meet-ups.