In recent years photo and video sharing web sites like Flickr and Youtube have become increasingly popular. Nowadays, every day several million photos are uploaded. These photos survey the world providing a visual index for the world for reconstruction and image based localization. However, given the scale of data we are facing significant challenges to process them within a short time frame given limited compute resources. In my talk I will present our work on the highly efficient organization for image based search and for reconstruction of 3D models from city scale photo collections (millions of images per city) on a single PC in the span of a day. Our reconstruction approach addresses a variety of the current challenges to achieve a concurrent 3D model from these data. For reconstruction from photo collections these challenges are: selecting the data of interest from the noisy datasets, efficient robust camera motion estimation, high performance stereo estimation from multiple views, as well as image based location recognition, matching for topology detection and the modeling of dynamic scene elements. In the talk I will discuss the details of our appearance and geometry based image organization method, our efficient stereo technique for determining the scene depths from photo collection images and their depth maps will also be explained during the talk. It allows to perform the scene depth estimation with multiple frames per second from a large set of views with a considerable variation in appearance. I will also discuss our approaches to reconstruct dynamic scene objects from internet photo and video collections to bring the 3D models alive.