svbrdf-estimation | SVBRDF Estimation

by mworchel Jupyter Notebook Version: Current License: MIT

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kandi X-RAY | svbrdf-estimation Summary

svbrdf-estimation is a Jupyter Notebook library. svbrdf-estimation has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

This is the repository to the WS 19/20 computer graphics project "SVBRDF Estimation using a Physically-based Differentiable Renderer" at Technische Universität Berlin (Technical University of Berlin). In the course of this project, the differentiable path tracer Redner [1] was integrated into the deep network-based SVBRDF estimation pipeline by Deschaintre et al. [2][3]. This repository contains custom PyTorch implementations of the single-view [2] as well as the multi-view method [3]. I used the reference code as a guidance.

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svbrdf-estimation has a low active ecosystem.

It has 13 star(s) with 6 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 17 have been closed. On average issues are closed in 8 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of svbrdf-estimation is current.

Quality

svbrdf-estimation has no bugs reported.

Security

svbrdf-estimation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

svbrdf-estimation is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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svbrdf-estimation releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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svbrdf-estimation Key Features

No Key Features are available at this moment for svbrdf-estimation.

svbrdf-estimation Examples and Code Snippets

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Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install svbrdf-estimation

In order to use the code, you will first need to set up an environment containing the required dependencies. If your are using conda, simply run. If you are using pip, you can install the requirements by running. Warning: While you will be able to run the code using the official pip package of Redner, the custom patch sampling camera (see documentation) will not be used. In order to enable this feature, you need to manually build and install Redner from source using the full-patch-sample-camera branch which is based on Redner 0.3.14. To run the training procedure on the toy dataset, execute the following scripts in the folder development/multiImage_pytorch. The trained model can by tested by running.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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