VisualEchoes: Spatial Image Representation Learning through Echolocation
This work addresses spatial reasoning in vision tasks for embodied agents, offering a novel, interaction-based approach that is not incremental.
The paper tackled the problem of learning spatial image representations by using echolocation cues, achieving results comparable to or better than heavily supervised pre-training on tasks like monocular depth estimation, surface normal estimation, and visual navigation.
Several animal species (e.g., bats, dolphins, and whales) and even visually impaired humans have the remarkable ability to perform echolocation: a biological sonar used to perceive spatial layout and locate objects in the world. We explore the spatial cues contained in echoes and how they can benefit vision tasks that require spatial reasoning. First we capture echo responses in photo-realistic 3D indoor scene environments. Then we propose a novel interaction-based representation learning framework that learns useful visual features via echolocation. We show that the learned image features are useful for multiple downstream vision tasks requiring spatial reasoning---monocular depth estimation, surface normal estimation, and visual navigation---with results comparable or even better than heavily supervised pre-training. Our work opens a new path for representation learning for embodied agents, where supervision comes from interacting with the physical world.