OX-NeRF attains the highest 3D reconstruction accuracy on ultra-sparse X-ray scenes with four to ten projections
The authors present OX-NeRF, which fuses a cross-scene convolutional encoder with a per-scene multi-resolution hash grid into an MLP and optimises them jointly by self-supervision, achieving higher 3D reconstruction accuracy than existing self-supervised radiance field methods on parallel-beam and cone-beam X-ray datasets in the ultra-sparse regime of four to ten projections.