The goal of this competition is to reconstruct accurate 3D maps. Last year’s Image Matching Challenge focused on two-view matching. This year you will take one step further: your task will be to reconstruct the 3D scene from many different views.
Your work could be the key to unlocking mapping the world from assorted and noisy data sources, such as images uploaded by users to services like Google Maps.
Your best camera may just be the phone in your pocket. You might take a snap of a landmark, then share it with friends. By itself, that photo is two-dimensional and only includes the perspective of your shooting location. Of course, many people may have taken photos of that same landmark. If we were able to combine all of our photos, we may be able to create a more complete, three-dimensional view of any given thing. Perhaps machine learning could help better capture the richness of the world using the vast amounts of unstructured image collections freely available on the internet.
The process of reconstructing a 3D model of an environment from a collection of images is called Structure from Motion (SfM). These images are often captured by trained operators or with additional sensor data, such as the cars used by Google Maps. This ensures homogeneous, high-quality data. It is much more difficult to build 3D models from assorted images, given a wide variety of viewpoints, along with lighting, weather, and other changes.
Competition host Google employs SfM techniques across many Google Maps services, such as the 3D models created from StreetView and aerial imagery. In order to accelerate research into this topic and better leverage the volume of data already publicly available, Google presents this competition in collaboration with Haiper and Kaggle.
Your work in helping to build accurate 3D models may have applications to photography, cultural heritage preservation, and many services across Google.
Awards:-
- 1st Place – $12,000
- 2nd Place – $10,000
- 3rd Place – $10,000
- 4th Place – $10,000
- 5th Place – $8,000
Deadline:- 06-06-2023
