Learning generalizable light field networks from few images
Q Li, F Multon, A Boukhayma - ICASSP 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
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 …
representation. Given a target camera pose, an implicit neural network maps each ray to its …
A Learning-based Method for Conditioning Neural Light Fields from Limited Inputs
Q Li, R Fu - IEEE Sensors Journal, 2024 - ieeexplore.ieee.org
This work proposes a novel approach for few-shot novel view synthesis based on a neural
light field representation. Our method leverages an implicit neural network to map each ray …
light field representation. Our method leverages an implicit neural network to map each ray …
Is vanilla MLP in neural radiance field enough for few-shot view synthesis?
Abstract Neural Radiance Field (NeRF) has achieved superior performance for novel view
synthesis by modeling the scene with a Multi-Layer Perception (MLP) and a volume …
synthesis by modeling the scene with a Multi-Layer Perception (MLP) and a volume …
Efficient Neural Light Fields (ENeLF) for Mobile Devices
A Peng - arXiv preprint arXiv:2406.00598, 2024 - arxiv.org
Novel view synthesis (NVS) is a challenge in computer vision and graphics, focusing on
generating realistic images of a scene from unobserved camera poses, given a limited set of …
generating realistic images of a scene from unobserved camera poses, given a limited set of …
Depth assisted novel view synthesis using few images
Q Li, R Fu, F Tang - Image and Vision Computing, 2024 - Elsevier
In this paper, we introduce a novel approach to improve the performance of Neural
Radiance Fields (NeRF) from limited input views. NeRF has exhibited impressive …
Radiance Fields (NeRF) from limited input views. NeRF has exhibited impressive …
NeFF: Neural Feature Fields for Few-shot View Synthesis Addressing the Shape-Radiance Ambiguity
We present NeFF, a 3D neural scene representation estimated from captured images.
Neural radiance fields (NeRF) have demonstrated their excellent performance for image …
Neural radiance fields (NeRF) have demonstrated their excellent performance for image …
R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis
Recent research explosion on Neural Radiance Field (NeRF) shows the encouraging
potential to represent complex scenes with neural networks. One major drawback of NeRF is …
potential to represent complex scenes with neural networks. One major drawback of NeRF is …
Manifoldnerf: View-dependent image feature supervision for few-shot neural radiance fields
D Kanaoka, M Sonogashira, H Tamukoh… - arXiv preprint arXiv …, 2023 - arxiv.org
Novel view synthesis has recently made significant progress with the advent of Neural
Radiance Fields (NeRF). DietNeRF is an extension of NeRF that aims to achieve this task …
Radiance Fields (NeRF). DietNeRF is an extension of NeRF that aims to achieve this task …
Learning neural light fields with ray-space embedding
Neural radiance fields (NeRFs) produce state-of-the-art view synthesis results, but are slow
to render, requiring hundreds of network evaluations per pixel to approximate a volume …
to render, requiring hundreds of network evaluations per pixel to approximate a volume …
Putting nerf on a diet: Semantically consistent few-shot view synthesis
We present DietNeRF, a 3D neural scene representation estimated from a few images.
Neural Radiance Fields (NeRF) learn a continuous volumetric representation of a scene …
Neural Radiance Fields (NeRF) learn a continuous volumetric representation of a scene …