neural-light-transport VS Deep-Illuminator

Compare neural-light-transport vs Deep-Illuminator and see what are their differences.

neural-light-transport

Neural Light Transport for Relighting and View Synthesis (by google)

Deep-Illuminator

Deep Illuminator is a data augmentation tool designed for image relighting. It can be used to easily and efficiently generate a wide range of illumination variants of a single image. (by chogovadze)
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neural-light-transport Deep-Illuminator
1 1
223 65
- -
10.0 10.0
almost 3 years ago over 2 years ago
Python Python
Apache License 2.0 -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

neural-light-transport

Posts with mentions or reviews of neural-light-transport. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-09-14.

Deep-Illuminator

Posts with mentions or reviews of Deep-Illuminator. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-09-14.

What are some alternatives?

When comparing neural-light-transport and Deep-Illuminator you can also consider the following projects:

Shadow-Mask-Face-Relighting - Official Code for Towards High Fidelity Face Relighting with Realistic Shadows (CVPR 2021)

LSMI-dataset - [ICCV'21] Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm under Mixed Illumination

nerfactor - Neural Factorization of Shape and Reflectance Under an Unknown Illumination

transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.