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Many species have evolved advanced non-visual perception while artificialsystems fall behind. Radar and ultrasound complement camera-based vision butthey are often too costly and complex to set up for very limited informationgain. In nature, sound is used effectively by bats, dolphins, whales, andhumans for navigation and communication. However, it is unclear how to bestharness sound for machine perception. Inspired by bats' echolocation mechanism,we design a low-cost BatVision system that is capable of seeing the 3D spatiallayout of space ahead by just listening with two ears. Our system emits shortchirps from a speaker and records returning echoes through microphones in anartificial human pinnae pair. During training, we additionally use a stereocamera to capture color images for calculating scene depths. We train a modelto predict depth maps and even grayscale images from the sound alone. Duringtesting, our trained BatVision provides surprisingly good predictions of 2Dvisual scenes from two 1D audio signals. Such a sound to vision system wouldbenefit robot navigation and machine vision, especially in low-light orno-light conditions. Our code and data are publicly available.

BatVision: Learning to See 3D Spatial Layout with Two Ears