Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GARF: Gaussian Activated Radiance Fields for 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.

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The categories of this paper are: quant-ph
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: nlin
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The categories of this paper are: physics
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The categories of this paper are: hep-th