Surf-NeRF: Surface Regularised Neural Radiance Fields

Neural Radiance Fields (NeRFs) provide a high fidelity, continuous scene representation that can realistically represent complex behaviour of light. Despite recent works like Ref-NeRF improving geometry through physics-inspired models, the ability for a NeRF to overcome shape-radiance ambiguity and converge to a representation consistent with real geometry remains limited. We demonstrate how curriculum learning of a surface light field model helps a NeRF converge towards a more geometrically accurate scene representation. We introduce four additional regularisation terms to impose geometric smoothness, consistency of normals and a separation of Lambertian and specular appearance at geometry in the scene, conforming to physical models. Our approach yields improvements of 14.4% to normals on positionally encoded NeRFs and 9.2% on grid-based models compared to current reflection-based NeRF variants. This includes a separated view-dependent appearance, conditioning a NeRF to have a geometric representation consistent with the captured scene. We demonstrate compatibility of our method with existing NeRF variants, as a key step in enabling radiance-based representations for geometry critical applications.

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

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.