psh-pytorch 0.0.2

Perfect Spatial Hashing in PyTorch

Perfect Spatial Hashing in PyTorch

This library is an unofficial implementation of Perfect Spatial Hashing by Lefebvre and Hoppe.

Usage

Install the library with:

# For the latest version:
pip install git+https://github.com/dli7319/psh-pytorch.git

# For the PyPI version:
# pip install psh-pytorch

Instantiate a PerfectSpatialHash object with:

from psh_pytorch import PerfectSpatialHash

occupancy_grid # A 2D (or higher dimensional) occupancy grid
out_features = 3
perfect_hash = PerfectSpatialHash(
    occupancy_grid, out_features)

# 2D forward pass
indices = torch.stack(torch.meshgrid(
    torch.arange(128, device=device),
    torch.arange(128, device=device),
    indexing='ij'), -1)
values, sparsity = perfect_hash(indices.reshape(-1, 2))
#   Values are of shape (128 * 128, 3)
#   Sparsity is of shape (128 * 128)
#   Note that values are unmasked
masked_values = values * sparsity.unsqueeze(-1)

Examples

See examples/ for a simple example of how to use the library.

Limitations

1. Currently, interpolation is not supported. Hence, you must use long indices and there are no gradients with respect to the indices.

Acknowledgement

If you find this library useful, please consider citing the original paper:

@article{lefebvre2006perfect,
author = {Lefebvre, Sylvain and Hoppe, Hugues},
title = {Perfect Spatial Hashing},
year = {2006},
issue_date = {July 2006},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {25},
number = {3},
issn = {0730-0301},
url = {https://doi.org/10.1145/1141911.1141926},
doi = {10.1145/1141911.1141926},
journal = {ACM Trans. Graph.},
month = {jul},
pages = {579–588},
numpages = {10},
keywords = {vector images, minimal perfect hash, sparse data, adaptive textures, multidimensional hashing, 3D-parameterized textures}
}