CVPR 2023, highlight paper - SPARF: Neural Radiance Fields from Sparse and Noisy Poses

In this episode we discuss SPARF: Neural Radiance Fields from Sparse and Noisy Poses by Prune Truong, Marie-Julie Rakotosaona, Fabian Manhardt, Federico Tombari. This paper introduces Sparse Pose Adjusting Radiance Field (SPARF), a method for synthesizing photorealistic novel views with only a few input images and noisy camera poses. SPARF uses multi-view geometry constraints to jointly learn the Neural Radiance Field (NeRF) and refine the camera poses. The approach sets a new state-of-the-art in the sparse-view regime on multiple challenging datasets by enforcing a global and geometrically accurate solution through a multi-view correspondence objective and depth consistency loss.