IGR VS neural-deferred-shading

Compare IGR vs neural-deferred-shading and see what are their differences.

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IGR neural-deferred-shading
1 1
372 239
- 5.4%
0.0 4.6
over 2 years ago 2 months ago
Python Python
- GNU General Public License v3.0 or later
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.

IGR

Posts with mentions or reviews of IGR. We have used some of these posts to build our list of alternatives and similar projects.

neural-deferred-shading

Posts with mentions or reviews of neural-deferred-shading. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing IGR and neural-deferred-shading you can also consider the following projects:

agi2nerf - Simple tool for converting Agisoft XML files to NERF JSON files for https://github.com/NVlabs/instant-ngp

ECON - [CVPR'23, Highlight] ECON: Explicit Clothed humans Optimized via Normal integration

Pix2Vox - The official implementation of "Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images". (Xie et al., ICCV 2019)

GAN2Shape - Code for GAN2Shape (ICLR2021 oral)

surface_normal_uncertainty - (ICCV 2021 - oral) Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

MICA - MICA - Towards Metrical Reconstruction of Human Faces [ECCV2022]

3d-transforms - 3D Transforms is a library to easily work with 3D data and make 3D transformations. This library originally started as a few functions here and there for my own work which I then turned into a library.

2dimageto3dmodel - We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

BlenderNeRF - Easy NeRF synthetic dataset creation within Blender