Geo-registered 3D models from crowdsourced image collections

Abstract

In this article we present our system for scalable, robust, and fast city-scale reconstruction from Internet photo collections (IPC) obtaining geo-registered dense 3D models. The major achievements of our system are the efficient use of coarse appearance descriptors combined with strong geometric constraints to reduce the computational complexity of the image overlap search. This unique combination of recognition and geometric constraints allows our method to reduce from quadratic complexity in the number of images to almost linear complexity in the IPC size. Accordingly, our 3D-modeling framework is inherently better scalable than other state of the art methods and in fact is currently the only method to support modeling from millions of images. In addition, we propose a novel mechanism to overcome the inherent scale ambiguity of the reconstructed models by exploiting geo-tags of the Internet photo collection images and readily available StreetView panoramas for fully automatic geo-registration of the 3D model. Moreover, our system also exploits image appearance clustering to tackle the challenge of computing dense 3D models from an image collection that has significant variation in illumination between images along with a wide variety of sensors and their associated different radiometric camera parameters. Our algorithm exploits the redundancy of the data to suppress estimation noise through a novel depth map fusion. The fusion simultaneously exploits surface and free space constraints during the fusion of a large number of depth maps. Cost volume compression during the fusion achieves lower memory requirements for high-resolution models. We demonstrate our system on a variety of scenes from an Internet photo collection of Berlin containing almost three million images from which we compute dense models in less than the span of a day on a single computer.