  There are a multitude of emerging imaging technologies that could benefit
robotics. However the need for bespoke models, calibration and low-level
processing represents a key barrier to their adoption. In this work we present
NOCaL, Neural odometry and Calibration using Light fields, a semi-supervised
learning architecture capable of interpreting previously unseen cameras without
calibration. NOCaL learns to estimate camera parameters, relative pose, and
scene appearance. It employs a scene-rendering hypernetwork pretrained on a
large number of existing cameras and scenes, and adapts to previously unseen
cameras using a small supervised training set to enforce metric scale. We
demonstrate NOCaL on rendered and captured imagery using conventional cameras,
demonstrating calibration-free odometry and novel view synthesis. This work
represents a key step toward automating the interpretation of general camera
geometries and emerging imaging technologies.
