  Scene-level novel view synthesis (NVS) is fundamental to many vision and
graphics applications. Recently, pose-conditioned diffusion models have led to
significant progress by extracting 3D information from 2D foundation models,
but these methods are limited by the lack of scene-level training data. Common
dataset choices either consist of isolated objects (Objaverse), or of
object-centric scenes with limited pose distributions (DTU, CO3D). In this
paper, we create a large-scale scene-level dataset from Internet photo
collections, called MegaScenes, which contains over 100K structure from motion
(SfM) reconstructions from around the world. Internet photos represent a
scalable data source but come with challenges such as lighting and transient
objects. We address these issues to further create a subset suitable for the
task of NVS. Additionally, we analyze failure cases of state-of-the-art NVS
methods and significantly improve generation consistency. Through extensive
experiments, we validate the effectiveness of both our dataset and method on
generating in-the-wild scenes. For details on the dataset and code, see our
project page at https://megascenes.github.io .
