  Embodied agents, trained to explore and navigate indoor photorealistic
environments, have achieved impressive results on standard datasets and
benchmarks. So far, experiments and evaluations have involved domestic and
working scenes like offices, flats, and houses. In this paper, we build and
release a new 3D space with unique characteristics: the one of a complete art
museum. We name this environment ArtGallery3D (AG3D). Compared with existing 3D
scenes, the collected space is ampler, richer in visual features, and provides
very sparse occupancy information. This feature is challenging for
occupancy-based agents which are usually trained in crowded domestic
environments with plenty of occupancy information. Additionally, we annotate
the coordinates of the main points of interest inside the museum, such as
paintings, statues, and other items. Thanks to this manual process, we deliver
a new benchmark for PointGoal navigation inside this new space. Trajectories in
this dataset are far more complex and lengthy than existing ground-truth paths
for navigation in Gibson and Matterport3D. We carry on extensive experimental
evaluation using our new space for evaluation and prove that existing methods
hardly adapt to this scenario. As such, we believe that the availability of
this 3D model will foster future research and help improve existing solutions.
