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Neural Radiance Field (NeRF) has recently emerged as a powerfulrepresentation to synthesize photorealistic novel views. While showingimpressive performance, it relies on the availability of dense input views withhighly accurate camera poses, thus limiting its application in real-worldscenarios. In this work, we introduce Sparse Pose Adjusting Radiance Field(SPARF), to address the challenge of novel-view synthesis given only fewwide-baseline input images (as low as 3) with noisy camera poses. Our approachexploits multi-view geometry constraints in order to jointly learn the NeRF andrefine the camera poses. By relying on pixel matches extracted between theinput views, our multi-view correspondence objective enforces the optimizedscene and camera poses to converge to a global and geometrically accuratesolution. Our depth consistency loss further encourages the reconstructed sceneto be consistent from any viewpoint. Our approach sets a new state of the artin the sparse-view regime on multiple challenging datasets.

SPARF: Neural Radiance Fields from Sparse and Noisy Poses