occupancy_networks | repository contains the code for the paper `` Occupancy | 3D Printing library

by autonomousvision Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | occupancy_networks Summary

occupancy_networks is a Python library typically used in Modeling, 3D Printing, Deep Learning applications. occupancy_networks has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

This repository contains the code for the paper "Occupancy Networks - Learning 3D Reconstruction in Function Space"

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occupancy_networks has a medium active ecosystem.

It has 1196 star(s) with 254 fork(s). There are 30 watchers for this library.

It had no major release in the last 6 months.

There are 73 open issues and 49 have been closed. On average issues are closed in 8 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of occupancy_networks is current.

Quality

occupancy_networks has 0 bugs and 0 code smells.

Security

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

occupancy_networks code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

occupancy_networks 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

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

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

occupancy_networks saves you 3871 person hours of effort in developing the same functionality from scratch.

It has 8248 lines of code, 465 functions and 119 files.

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

Top functions reviewed by kandi - BETA

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

Parse command line arguments
Get the full table
Get a triangle table
Evaluate the model
Return a model instance
Calculate loss for training
Evaluate a mesh
Compute the distance between a point cloud
Save a mesh figure
Write mesh to off file
Create a dataset
Returns the inputs field for the given mode
Runs the simulation
Visualize the model
Transform a rotation matrix
Generate a mesh from input data
Read data from a file - like object
Get the projection matrix
Compute the distance between point cloud and target point cloud
Get the triangle table
Estimate the distance between two points
Read a numpy array from a file - like object
Extract a mesh from the current mesh
Get a model from the given configuration
Get unique triangles
Process a trimesh file
Calculate the distance between a triangle and a triangle

Get all kandi verified functions for this library.

occupancy_networks Key Features

No Key Features are available at this moment for occupancy_networks.

occupancy_networks Examples and Code Snippets

No Code Snippets are available at this moment for occupancy_networks.

Community Discussions

Trending Discussions on occupancy_networks

How to Install older version of Meshlab?

Compiling with Cython using OpenMP on macOS

QUESTION

How to Install older version of Meshlab?

Asked 2022-Mar-05 at 02:40

Ciao Paolo,

thanks for the amazing work with meshlab,

I was trying OccNet, and I run into this issue

...

ANSWER

Answered 2022-Mar-04 at 17:11

You can find the older versions of MeshLab in the Releases Tab

Source https://stackoverflow.com/questions/71347545

QUESTION

Compiling with Cython using OpenMP on macOS

Asked 2020-Oct-15 at 15:58

I'm on macOS Mojave 10.14.6 and I'm trying to compile some required extensions modules in c and c++ from this repository with:

python setup.py build_ext --inplace

which gives me the following error:

...

ANSWER

Answered 2020-Oct-15 at 15:58

Here are a few hints:

Use gcc instead of llvm or clang for painless openmp-support on macOS. Note that apple's default gcc is just an alias for Apple clang as you'll see with gcc --version. You can install the real gcc with homebrew: brew install gcc.
Then use export CC='gcc-10' (the newest version should be gcc 10.x) inside the same terminal window to use homebrew's gcc temporarily as your C compiler.
There's no need to set CXXFLAGS or CFLAGS. The required flags are set by distutils/setuptools inside the setup.py.
You won't be able to compile dmc_cuda_module on macOS 10.14.6. The latest macOS version nvidia offers cuda drivers for is 10.13.6. So you might uncomment this part of the setup.py and hope for the best you don't need this module...
Some of the Extensions inside the setup.py aren't including the numpy headers while using the numpy C-API. On macOS it's necessary to include the numpy headers for each Extension, see this comment. So you have to add include_dirs=[numpy_include_dir] to those Extensions.
Edit: As discussed in the chat: The error was due to the conda env ignoring the CC variable. After installing python+pip via homebrew and the required python packages via pip, this answer's steps worked for the OP.

All in all, here's a setup.py that worked for me (macOS 10.5.7, gcc-10):

Source https://stackoverflow.com/questions/64350106

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

Vulnerabilities

No vulnerabilities reported

Install occupancy_networks

First you have to make sure that you have all dependencies in place. The simplest way to do so, is to use anaconda. You can create an anaconda environment called mesh_funcspace using. Next, compile the extension modules. You can do this via. To compile the dmc extension, you have to have a cuda enabled device set up. If you experience any errors, you can simply comment out the dmc_* dependencies in setup.py. You should then also comment out the dmc imports in im2mesh/config.py.

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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