  Self-driving vehicles rely on urban street maps for autonomous navigation. In
this paper, we introduce Pix2Map, a method for inferring urban street map
topology directly from ego-view images, as needed to continually update and
expand existing maps. This is a challenging task, as we need to infer a complex
urban road topology directly from raw image data. The main insight of this
paper is that this problem can be posed as cross-modal retrieval by learning a
joint, cross-modal embedding space for images and existing maps, represented as
discrete graphs that encode the topological layout of the visual surroundings.
We conduct our experimental evaluation using the Argoverse dataset and show
that it is indeed possible to accurately retrieve street maps corresponding to
both seen and unseen roads solely from image data. Moreover, we show that our
retrieved maps can be used to update or expand existing maps and even show
proof-of-concept results for visual localization and image retrieval from
spatial graphs.
