Fix: Improve performance for non-contiguous NumPy arrays in pybind vector converters

[Khizar1]

…rs_*

Type

• Bug fix (non-breaking change which fixes an issue): Fixes open3d.utility.Vector3dVector() sometimes slow #5250
• New feature (non-breaking change which adds functionality). Resolves #
• Breaking change (fix or feature that would cause existing functionality to not work as expected) Resolves #

Motivation and Context

This PR fixes slow performance when constructing Open3D vectors (Vector3dVector) from non-contiguous NumPy arrays, such as Pandas DataFrames. Previously, pybind11 performed slow element-by-element deep copies due to the restrictive py::array::c_style flag in py_array_to_vectors_*. This change relaxes that restriction and performs a lightweight runtime check instead:

• If the array is already contiguous -> no copy.
• If not -> a single fast contiguous copy via NumPy.

Up to ~50× faster conversion for non-contiguous arrays. Please refer to benchmark results below.

Checklist:

• I have run python util/check_style.py --apply to apply Open3D code style
  to my code.
• This PR changes Open3D behavior or adds new functionality.
  • Both C++ (Doxygen) and Python (Sphinx / Google style) documentation is
    updated accordingly.
  • I have added or updated C++ and / or Python unit tests OR included test
    results (e.g. screenshots or numbers) here.
• I will follow up and update the code if CI fails.
• For fork PRs, I have selected Allow edits from maintainers.

Description

This PR improves array conversion performance in cpp/pybind/utility/eigen.cpp by replacing
the static py::array::c_style flag with a runtime check for contiguity.

Summary of Changes

• Updated py_array_to_vectors_double , py_array_to_vectors_int, py_array_to_vectors_int_eigen_allocator and py_array_to_vectors_int64_eigen_allocator to:
  • Detect C-contiguous arrays at runtime (array.flags()).
  • Copy only if the array is not c-contagious.
• No behavior change for contiguous inputs.

Benchmark Results

Vector Type	Before (ms, Pandas -> NumPy)	After (ms)	Speedup
Vector2dVector	2602.73	126.28	~21×
Vector2iVector	2502.89	49.43	~51×
Vector3dVector	2640.17	164.48	~16×
Vector3iVector	2607.42	69.98	~37×
Vector4iVector	2567.23	85.04	~30×

Reproducible script: benchmark_all_vectors.py

Tested on Ubuntu 24.04, Python 3.12 inside a Docker environment.

[update-docs]

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