neural-splines | Official Implementation of Neural Splines

by fwilliams Python Version: Current License: MIT

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

kandi X-RAY | neural-splines Summary

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

This repository contains the official implementation of the CVPR 2021 (Oral) paper Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networks.

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Support

neural-splines has a low active ecosystem.

It has 5 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 2 have been closed. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of neural-splines is current.

Quality

neural-splines has no bugs reported.

Security

neural-splines has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

neural-splines 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

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

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

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Evaluate a model on a grid
Transform a pointcloud
Fit a model to a point cloud
Runs a Falkon fit
Generate Nystm samples
K - means algorithm
Calculate the triplet points along the given eps
Normalize a pointcloud transform
Generates the voxel chunks of the grid
Load a point cloud from a file
Compute weights for a given bounding box
Calculate cell weights using trilinear
Return the bounding box of a point cloud
Scale bounding box of bounding box
Returns a boolean mask of points in bbox

Get all kandi verified functions for this library.

neural-splines Key Features

No Key Features are available at this moment for neural-splines.

neural-splines Examples and Code Snippets

No Code Snippets are available at this moment for neural-splines.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install neural-splines

⚠️ WARNING ⚠️ Due to a bug in KeOps, the first time you use any code in this repository will throw a ModuleNotFoundError. All subsequent invocations of Neural Splines should work.
Download and unzip the example point clouds here
Unzip the file, in the directory of this repository, which should produce a directory named demo_data
Run python fit.py demo_data/bunny.ply 10_000 128 On the first run this will fail (see above, just rerun it). On the second run it will compile some kernels and then produce a file called recon.ply which should be a reconstructed Stanford Bunny. The image below shows the input points and reconstruction for the bunny,
Run python fit-grid.py demo_data/living_room_33_500_per_m2.ply 10_000 512 8 which will produce another recon.ply mesh, this time of a full room as shown below.

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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