mlsanityData to 7 Oct 2026

Learning Generalizable Light Field Networks from Few Images

arXiv 2207.11757 ↗First posted 24 Jul 2022cs.CV cs.AI cs.GRcited by 10

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Abstract (arXiv)

We explore a new strategy for few-shot novel view synthesis based on a neural light field representation. Given a target camera pose, an implicit neural network maps each ray to its target pixel's color directly. The network is conditioned on local ray features generated by coarse volumetric rendering from an explicit 3D feature volume. This volume is built from the input images using a 3D ConvNet. Our method achieves competitive performances on synthetic and real MVS data with respect to state-of-the-art neural radiance field based competition, while offering a 100 times faster rendering.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 45 of its 58 references that are in the map. First posted on arXiv 24 Jul 2022.

Builds on

45 of 58 references.

References · 45

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33 more
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Citing papers per year: 2022: 1; 2023: 0; 2024: 5; 2025: 4.

10 papers.