  Neural Radiance Field (NeRF) has recently emerged as a powerful
representation to synthesize photorealistic novel views. While showing
impressive performance, it relies on the availability of dense input views with
highly accurate camera poses, thus limiting its application in real-world
scenarios. In this work, we introduce Sparse Pose Adjusting Radiance Field
(SPARF), to address the challenge of novel-view synthesis given only few
wide-baseline input images (as low as 3) with noisy camera poses. Our approach
exploits multi-view geometry constraints in order to jointly learn the NeRF and
refine the camera poses. By relying on pixel matches extracted between the
input views, our multi-view correspondence objective enforces the optimized
scene and camera poses to converge to a global and geometrically accurate
solution. Our depth consistency loss further encourages the reconstructed scene
to be consistent from any viewpoint. Our approach sets a new state of the art
in the sparse-view regime on multiple challenging datasets.
