A new algorithm for view synthesis based on Neural Radiance Field (NeRF)

🚀 Feature

An implementation of the algorithm in Wang et al. (2021) - https://arxiv.org/abs/2102.07064 - for Neural Radiance Field view synthesis without known camera parameters

Motivation

We would like the have a simple API where you pass a sequence of images (batch, list of frames, etc.) and you get back the optimized camera intrinsic so that can be used jointly with undistort_image (by @jhacsonmeza). For the nature of Nerf-- seems that the relative poses (or respect to world) could be also retrieved.

Pitch

Create the class CameraCalibrator to implement Algorithm 1 in Wang et al. (2021)

Alternatives

Additional context

Preliminary usage design

from kornia.contrib import CameraCalibrator

calib = CameraCalibrator(...)
calib.add_image(load_image(...))
....  # BxCxHxW

res = calib.run()  # perform nerf training
res['K']  # intrinsics Bx3x3
res['P']  # projection Bx3x4

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Consider also to contribute to Kornia universe projects 😃

• Tutorials: our repository containing the tutorials.

[ducha-aiki]

I’d go for the 1), at least in the beginning

[edgarriba]

great ! Isn’t colab enough ? I could do some local testings with my workstation.

Go for a PR (and mark as draft) - I think it’s the best to discuss the final shape of the API.