InverseRenderingOfIndoorScene | Zhengqin Li , Mohammad Shafiei

by lzqsd Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | InverseRenderingOfIndoorScene Summary

InverseRenderingOfIndoorScene is a Python library. InverseRenderingOfIndoorScene has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However InverseRenderingOfIndoorScene build file is not available. You can download it from GitHub.

Zhengqin Li, Mohammad Shafiei, Ravi Ramamoorthi, Kalyan Sunkavalli, Manmohan Chandraker.

Support

Quality

Security

License

Reuse

Support

InverseRenderingOfIndoorScene has a low active ecosystem.

It has 231 star(s) with 30 fork(s). There are 13 watchers for this library.

It had no major release in the last 6 months.

There are 8 open issues and 6 have been closed. On average issues are closed in 22 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of InverseRenderingOfIndoorScene is current.

Quality

InverseRenderingOfIndoorScene has 0 bugs and 0 code smells.

Security

InverseRenderingOfIndoorScene has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

InverseRenderingOfIndoorScene code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

InverseRenderingOfIndoorScene is licensed under the MIT License. This license is Permissive.

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

Reuse

InverseRenderingOfIndoorScene releases are not available. You will need to build from source code and install.

InverseRenderingOfIndoorScene has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are available. Examples and code snippets are not available.

InverseRenderingOfIndoorScene saves you 2764 person hours of effort in developing the same functionality from scratch.

It has 5984 lines of code, 84 functions and 26 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed InverseRenderingOfIndoorScene and discovered the below as its top functions. This is intended to give you an instant insight into InverseRenderingOfIndoorScene implemented functionality, and help decide if they suit your requirements.

Wrapper function for wrapper function
Perform forward evaluation
Linear Regression
Convert array to Env format
Linear Regression
Backward computation
Solve the gradient of a matrix
Solve quadratic solver
Blursively blur each vertex
Wrapper for ImageW
BatchRank loss function
Turn the error into a numpy array
Bistochastic bistastochastic
Writes images to hdf5 file
Write npErr to screen
Write npErr to file out
Compute factorization
Write errorArr to fileOut
Write errorArr to screen
Compute the weight of a reflectance
Writes env to file
Compute the predicted image
Convert the predefined pred to shading
Perform forward and normalization
Wrapper function for numpy
Wrapper function for BRDF
Apply blur to image

Get all kandi verified functions for this library.

InverseRenderingOfIndoorScene Key Features

No Key Features are available at this moment for InverseRenderingOfIndoorScene.

InverseRenderingOfIndoorScene Examples and Code Snippets

No Code Snippets are available at this moment for InverseRenderingOfIndoorScene.

Community Discussions

No Community Discussions are available at this moment for InverseRenderingOfIndoorScene.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install InverseRenderingOfIndoorScene

The trained models can be downloaded from the link. To test the models, please copy the models to the same directory as the code and run the commands as shown below.

Support

Project page: http://cseweb.ucsd.edu/~visco…/projects/CVPR20InverseIndoor/Trained models: http://cseweb.ucsd.edu/~viscomp/projects/CVPR20InverseIndoor/models.zipRenderer: https://github.com/lzqsd/OptixRendererObject insertion: https://github.com/lzqsd/VirtualObjectInsertionTileable texture synthesis: https://github.com/lzqsd/TileableTextureSynthesisSpherical gaussian optimization: https://github.com/lzqsd/SphericalGaussianOptimizationDataset: https://ucsd-openrooms.github.io/

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