GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation
This addresses the need for more efficient and accurate pose estimation in 3D scene reconstruction, though it appears incremental as it builds on existing joint recovery approaches like BARF.
The paper tackles the problem of jointly recovering neural radiance fields and camera poses without accurate prior poses, presenting GARF, which outperforms state-of-the-art methods in high-fidelity reconstruction and pose estimation.
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.