pureini

CloudINI-compatible point-cloud compression for Python, implemented as a Rust/PyO3 extension.

  • Reads CloudINI V2, V3, V4, and V5 payloads.
  • Writes the current V5 format by default, with V2-V4 output available through EncodingInfo.version.
  • Supports no second-stage compression, LZ4, and Zstandard.
  • Has no Python runtime dependencies and does not require NumPy, Numba, or LLVM.
  • Uses Python's stable ABI for CPython 3.10 and newer.
import pureini

info = pureini.EncodingInfo(
    width=1,
    height=1,
    point_step=4,
    fields=[
        pureini.PointField(
            name="x",
            offset=0,
            type=pureini.FieldType.FLOAT32,
            resolution=0.001,
        )
    ],
)

encoded = pureini.PointcloudEncoder(info).encode(b"\0\0\0\0")
decoded, decoded_info = pureini.PointcloudDecoder().decode(encoded)

The public API is deliberately limited to PointField, EncodingInfo, FieldType, EncodingOptions, CompressionOption, PointcloudEncoder, and PointcloudDecoder. Wire-format primitives and header helpers are internal.

Point-cloud cleanup can be fused into the native encode call. This removes invalid XYZ returns, stably groups retained points by a discrete field, and returns the updated layout together with the payload:

encoded, output_point_count, did_filter_invalid_xyz = pureini.PointcloudEncoder(info).encode(
    cloud_data,
    drop_invalid=True,
    sort_field="line",
    return_metadata=True,
)

return_metadata=True opts into the tuple result. Without it, encode returns bytes directly even when preprocessing is enabled. output_point_count is None when preprocessing leaves the input layout unchanged. point_step never changes, and transformed clouds are one row high.

Source point data is little-endian by default. Pass is_bigendian=True to normalize big-endian fields in Rust before preprocessing or encoding.

Native preprocessing is opt-in and releases the GIL; larger counting-sort workloads run in parallel.

Add it to a project with uv add pureini.

Distribution

Release artifacts use CPython's stable ABI and cover Python 3.10 and newer:

  • manylinux and musllinux wheels for x86-64 and AArch64;
  • macOS wheels for Intel and Apple Silicon;
  • Windows x86-64 wheels;
  • a source distribution containing the complete Rust crate.

CI tests native wheels on CPython 3.10 and 3.14 and independently rebuilds and tests the source distribution.

Benchmarking

Benchmark a release wheel rather than the editable debug build:

uv build --package pureini --out-dir dist
uv run --isolated --no-project --with dist/pureini-0.8.0-*.whl \
  python pureini/benchmarks/benchmark_matrix.py

The matrix uses warmed median timings for structured and random integers, lossy and lossless XYZ, LZ4, Zstandard, and mixed LiDAR fields. Adjust --points, --iterations, and --warmups for the target machine.

Compare the fused Rust preprocessing path against the previous warmed Numba pipeline and require a material win:

uv run --with numba python pureini/benchmarks/benchmark_preprocessing.py \
  --points 500000 --iterations 30 --warmups 8 --minimum-speedup 1.25

The benchmark validates decoded bytes and layout before timing. Its default fixture contains 20% invalid XYZ placeholders and 128 interleaved line groups.

Project links

Data verified by PyPI on Jul 30, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.10.0)

1 maintainer

PyPI data
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Avatar for mrkbac from gravatar.com mrkbac

Credits

Author: Marko Bausch

GitHub Statistics

Data verified by PyPI on Jul 30, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

GPL-3.0

Requires

Python >=3.10

Classifiers

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