Metadata-Version: 2.1
Name: pcdiff
Version: 1.0.1
Summary: Simple and small library to compute differential operators (gradient, divergence, Laplacian) on point clouds.
Home-page: https://github.com/rubenwiersma/pcdiff
Author: Ruben Wiersma
Author-email: rubenwiersma@gmail.com
License: MIT
Project-URL: Bug Tracker, https://github.com/rubenwiersma/pcdiff
Description: # `pcdiff`: Differential operators on point clouds
        Simple and small library to compute differential operators (gradient, divergence, Laplacian) on point clouds.
        
        ## Installation
        The recommended installation method is by using pip:
        ```bash
        pip install pcdiff
        ```
        
        ## Example usage
        See `demo.py` in `examples` for a full visual demo. For a quick start:
        ```python
        import numpy as np
        from pcdiff import knn_graph, estimate_basis, build_grad_div
        
        # Random point cloud
        pos = np.random.rand(1000, 3)
        
        # Generate kNN graph
        edge_index = knn_graph(pos, 20)
        # Estimate normals and local frames
        basis = estimate_basis(pos, edge_index)
        # Build gradient and divergence operators (Scipy sparse matrices)
        grad, div = build_grad_div(pos, *basis, edge_index)
        
        # ... use gradient and divergence in any task you like
        ```
        
        For sake of simplicity, every operation is written in Numpy and can be accelerated with Numba or Jax. If you would like to use these operators in PyTorch, please refer the github repository for [DeltaConv](https://github.com/rubenwiersma/deltaconv): `pip install deltaconv` and use the operators from `deltaconv.geometry`.
        
        ## Citation
        If you find this library useful in your own work, please cite our paper on DeltaConv, a convolution for point clouds that uses these operators:
        
        ```bib
        @Article{Wiersma2022DeltaConv,
          author    = {Ruben Wiersma, Ahmad Nasikun, Elmar Eisemann, Klaus Hildebrandt},
          journal   = {Transactions on Graphics},
          title     = {DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds},
          year      = {2022},
          month     = jul,
          number    = {4},
          volume    = {41},
          doi       = {10.1145/3528223.3530166},
          publisher = {ACM},
        }
        ```
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
