Just yesterday, Google announced a new challenge.
Reconstructing 3D objects and buildings from a series of images is a well-known problem in computer vision, known as Structure-from-Motion (SfM). It has diverse applications in photography and cultural heritage preservation (e.g., allowing people to explore the sculptures of Rapa Nui in a browser) and powers many services across Google Maps, such as the 3D models created from StreetView and aerial imagery. In these examples, images are usually captured by operators under controlled conditions. While this ensures homogeneous data with a uniform, high-quality appearance in the images and the final reconstruction, it also limits the diversity of sites captured and the viewpoints from which they are seen. What if, instead of using images from tightly controlled conditions, one could apply SfM techniques to better capture the richness of the world using the vast amounts of unstructured image collections freely available on the internet?
We hope this benchmark, dataset and challenge helps advance the state of the art in 3D reconstruction with heterogeneous images. If you’re interested in participating in the challenge, please see the 2020 Image Matching Challenge website for more details.