Neural Radiance Fields (NeRF) have demonstrated very impressive performance
in novel view synthesis via implicitly modelling 3D representations from
multi-view 2D images. However, most existing studies train NeRF models with
either reasonable camera pose initialization or manually-crafted camera pose
distributions which are often unavailable or hard to acquire in various
real-world data. We design VMRF, an innovative view matching NeRF that enables
effective NeRF training without requiring prior knowledge in camera poses or
camera pose distributions. VMRF introduces a view matching scheme, which
exploits unbalanced optimal transport to produce a feature transport plan for
mapping a rendered image with randomly initialized camera pose to the
corresponding real image. With the feature transport plan as the guidance, a
novel pose calibration technique is designed which rectifies the initially
randomized camera poses by predicting relative pose transformations between the
pair of rendered and real images. Extensive experiments over a number of
synthetic and real datasets show that the proposed VMRF outperforms the
state-of-the-art qualitatively and quantitatively by large margins.

VMRF is an innovative NeRF that can effectively train without requiring prior knowledge in camera poses or camera pose distributions through its view matching scheme and feature transport plan.