Papers › GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation
GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation
Shin-Fang Chng, Sameera Ramasinghe, Jamie Sherrah, Simon Lucey
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Novel View Synthesis | BLEFF | GARF | PSNR/SSIM | 25.80/0.76 | #3 of 3 | Archive leaderboard | report |
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