  Despite Neural Radiance Fields (NeRF) showing compelling results in
photorealistic novel views synthesis of real-world scenes, most existing
approaches require accurate prior camera poses. Although approaches for jointly
recovering the radiance field and camera pose exist (BARF), they rely on a
cumbersome coarse-to-fine auxiliary positional embedding to ensure good
performance. We present Gaussian Activated neural Radiance Fields (GARF), a new
positional embedding-free neural radiance field architecture - employing
Gaussian activations - that outperforms the current state-of-the-art in terms
of high fidelity reconstruction and pose estimation.
