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There are a multitude of emerging imaging technologies that could benefitrobotics. However the need for bespoke models, calibration and low-levelprocessing represents a key barrier to their adoption. In this work we presentNOCaL, Neural odometry and Calibration using Light fields, a semi-supervisedlearning architecture capable of interpreting previously unseen cameras withoutcalibration. NOCaL learns to estimate camera parameters, relative pose, andscene appearance. It employs a scene-rendering hypernetwork pretrained on alarge number of existing cameras and scenes, and adapts to previously unseencameras using a small supervised training set to enforce metric scale. Wedemonstrate NOCaL on rendered and captured imagery using conventional cameras,demonstrating calibration-free odometry and novel view synthesis. This workrepresents a key step toward automating the interpretation of general camerageometries and emerging imaging technologies.

NOCaL: Calibration-Free Semi-Supervised Learning of Odometry and Camera Intrinsics