Neural World Models for Computer Vision
Anthony Hu
Humans navigate in their environment by learning a mental model of the world
through passive observation and active interaction. Their world model allows
them to anticipate what might happen next and act accordingly with respect to
an underlying objective. Such world models hold strong promises for planning in
complex environments like in autonomous driving. A human driver, or a
self-driving system, perceives their surroundings with their eyes or their
cameras. They infer an internal representation of the world which should: (i)
have spatial memory (e.g. occlusions), (ii) fill partially observable or noisy
inputs (e.g. when blinded by sunlight), and (iii) be able to reason about
unobservable events probabilistically (e.g. predict different possible
futures). They are embodied intelligent agents that can predict, plan, and act
in the physical world through their world model. In this thesis we present a
general framework to train a world model and a policy, parameterised by deep
neural networks, from camera observations and expert demonstrations. We
leverage important computer vision concepts such as geometry, semantics, and
motion to scale world models to complex urban driving scenes.
  First, we propose a model that predicts important quantities in computer
vision: depth, semantic segmentation, and optical flow. We then use 3D geometry
as an inductive bias to operate in the bird's-eye view space. We present for
the first time a model that can predict probabilistic future trajectories of
dynamic agents in bird's-eye view from 360{\deg} surround monocular cameras
only. Finally, we demonstrate the benefits of learning a world model in
closed-loop driving. Our model can jointly predict static scene, dynamic scene,
and ego-behaviour in an urban driving environment.