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arXiv · 2204.05735

GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation

Shin-Fang Chng·Sameera Ramasinghe·Jamie Sherrah·Simon Lucey
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Abstract

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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Shin-Fang Chng, Sameera Ramasinghe, Jamie Sherrah, Simon Lucey. 2022-04-12. GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation. https://arxiv.org/abs/2204.05735

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