  Many species have evolved advanced non-visual perception while artificial
systems fall behind. Radar and ultrasound complement camera-based vision but
they are often too costly and complex to set up for very limited information
gain. In nature, sound is used effectively by bats, dolphins, whales, and
humans for navigation and communication. However, it is unclear how to best
harness sound for machine perception. Inspired by bats' echolocation mechanism,
we design a low-cost BatVision system that is capable of seeing the 3D spatial
layout of space ahead by just listening with two ears. Our system emits short
chirps from a speaker and records returning echoes through microphones in an
artificial human pinnae pair. During training, we additionally use a stereo
camera to capture color images for calculating scene depths. We train a model
to predict depth maps and even grayscale images from the sound alone. During
testing, our trained BatVision provides surprisingly good predictions of 2D
visual scenes from two 1D audio signals. Such a sound to vision system would
benefit robot navigation and machine vision, especially in low-light or
no-light conditions. Our code and data are publicly available.
