awesome-nerf / awesome-nerf Goto Github PK

https://jonbarron.info/zipnerf/

Hi. Thanks for the great repository.
Indeed, I found an error on author information of my publication.
Could you change the main author of Self-calibrating Neural Radiance Fields from Jaesik to Yoonwoo?
In detail,

Self-Calibrating Neural Radiance Fields, Park et al., ICCV 2021 | github | bibtex -> Self-Calibrating Neural Radiance Fields, Jeong et al., ICCV 2021 | github | bibtex

Thanks.

hello, Is there some one know how to extract high quality color mesh? very thank you.

I'm not the original author and I don't have time to figure out your rules for PRs so I thought I'd just dump this in an issue:

https://github.com/thomasneff/AdaNeRF

Very Looking forward to seeing updateing the NeRF recently papers

Best!

There's some good PRs but no updates in several months.

I volunteer to be a temporary custodian if it moves things forward but I'm sure there are better qualified people who would be happy to take over.

Hi, Lin. Thanks for the great work! Really appreciate for this awesome repo

Could you please kindly add some more nerfs to pose estimation? Many thanks!

Neural Refinement for Absolute Pose Regression with Feature Synthesis (CVPR 2024)
Project web: https://nefes.active.vision/
Github: https://github.com/ActiveVisionLab/NeFeS

PoRF: Pose Residual Field for Accurate Neural Surface Reconstruction (ICLR 2024)
Project web: https://porf.active.vision/
Github: https://github.com/ActiveVisionLab/porf/

CROSSFIRE: Camera Relocalization On Self-Supervised Features from an Implicit Representation (ICCV 2023)
https://arxiv.org/abs/2303.04869

NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior (CVPR 2023 Highlight)
Project web: https://nope-nerf.active.vision/
Github: https://github.com/ActiveVisionLab/nope-nerf/

GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation (ECCV 2022)
https://arxiv.org/abs/2204.05735

DFNet: Enhance Absolute Pose Regression with Direct Feature Matching (ECCV 2022)
Project web: https://dfnet.active.vision/
Github: https://github.com/ActiveVisionLab/DFNet

https://www.youtube.com/watch?v=zNvS5hQsWEo&list=PLkcK4tsjESpT3PWVo62UudYMZmU1D9b0j

Thanks for your job!

https://arxiv.org/abs/2011.10379

Hi, I recently started implementing NeRV,but it is hard for me,Do you plan to implement NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis?

Thanks for this awesome project. Could you please add our CVPR 2023 highlight paper: Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion? The paper is about generating nerf using diffusion model.

Arxiv: https://arxiv.org/abs/2212.06135
Project page: https://3d-avatar-diffusion.microsoft.com/

https://arxiv.org/abs/2012.03927

https://people.eecs.berkeley.edu/~pratul/nerv/

Great job! Thanks for your sharing. It helps me a lot.

Thanks for your exellent work!
I want to ask you how to apply NeRF in the field of automatic driving.

Hi, I recently started implementing NeRF-W, and my experiment (on the lego dataset, section D) seems working successfully: https://github.com/kwea123/nerf_pl/blob/nerfw/test_nerfu_occ.ipynb

Currently I only tested NeRF-U with occluders, the code is ready for other experiments, I just need some more time to run all the trainings.

My branch: https://github.com/kwea123/nerf_pl/tree/nerfw

It would be great if you can include this as a reference source to NeRF-W, as it seems that they won't publish the code (and the data).

Update: NeRF-A with color perturbation also passed. https://github.com/kwea123/nerf_pl/blob/nerfw/test_nerfa_color.ipynb

Update: NeRF-W with color perturbation & occluder passed. https://github.com/kwea123/nerf_pl/blob/nerfw/test_nerfw_all.ipynb

These experiments should be enough to demonstrate that the implementation is almost correct.

https://arxiv.org/pdf/2010.00450.pdf

Hey guys,

We sincerely appreciate the value of your repository! We would like to kindly request the inclusion of our customizable SLAM robustness benchmark, which incorporates a noisy data simulation pipeline for a comprehensive evaluation of SLAM performance. Our benchmark mainly focuses on evaluating the robustness of the latest monocular/multi-modal Neural-based SLAM models, including NeRF-based and Gaussian-Splatting-based SLAM, which reveals some intriguing properties of NeRF and GS representation for instant 3D reconstruction and localization. Additionally, it offers a preliminary assessment of multi-view and multi-agent/collaborative SLAM robustness.

We are confident that the addition of our benchmark will contribute to making SLAM more deployable and robust. We greatly appreciate your consideration. Thanks!

• Customizable-SLAM: Customizable Perturbation Synthesis for Robust SLAM Benchmarking, ArXiV, 2024. [Paper] [Code] [Video]

I heard that NeRF is very slow at test time, "Rendering a single 1280x720 image on one such GPU takes ∼30 seconds at test time ".

Can we just generate low resolution images and then use some super resolution method to improve the resolution ? That may make it faster.