Where to Look Next: Unsupervised Active Visual Exploration on 360{\deg} Input

Understand

We address the problem of active visual exploration of large 360{\deg} inputs.

• In our setting an active agent with a limited camera bandwidth explores its 360{\deg} environment by changing its viewing direction at limited discrete time steps.
• As such, it observes the world as a sequence of narrow field-of-view 'glimpses', deciding for itself where to look next.
• Our proposed method exceeds previous works' performance by a significant margin without the need for deep reinforcement learning or training separate networks as sidekicks.