Add point cloud smoothing algorithms

[TsofnatMaman]

Type

• Bug fix (non-breaking change which fixes an issue): Fixes #
• New feature (non-breaking change which adds functionality). Resolves Possible to add point cloud smoothing, like the one in PCL?  #4061
• Breaking change (fix or feature that would cause existing functionality to not work as expected) Resolves #

Motivation and Context

Open3D currently lacks point cloud smoothing operations similar to those available in PCL.
This PR adds several point cloud smoothing algorithms, enabling denoising and improved
downstream processing such as surface reconstruction and registration.

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 introduces multiple point cloud smoothing methods to Open3D:

Added smoothing methods

• PointCloud::SmoothMLS (Moving Least Squares)
• PointCloud::SmoothLaplacian
• PointCloud::SmoothTaubin
• PointCloud::SmoothBilateral

Implementation details

• MLS: Uses KDTree neighborhood search (radius/knn/hybrid) and PCA to compute a local tangent plane, then projects points onto the plane.
• Laplacian: Iteratively updates points towards the centroid of k-nearest neighbors.
• Taubin: Applies a lambda pass followed by a mu pass per iteration (feature-preserving smoothing).
• Bilateral: Uses spatial and range weights; requires normals and estimates them if missing.

Tests

• Added unit tests covering all smoothing methods.
• All tests pass locally.

Usage examples (Python)

import open3d as o3d

pcd = o3d.io.read_point_cloud("cloud.ply")

# MLS smoothing
smoothed_mls = pcd.smooth_mls(o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30))

# Laplacian smoothing
smoothed_lap = pcd.smooth_laplacian(iterations=5, lambda=0.5, knn=30)

# Taubin smoothing
smoothed_taubin = pcd.smooth_taubin(iterations=5, lambda=0.5, mu=-0.53, knn=30)

# Bilateral smoothing
smoothed_bilateral = pcd.smooth_bilateral(
    o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30),
    sigma_s=0.1,
    sigma_r=0.05,
)
o3d.io.write_point_cloud("cloud_smoothed.ply", smoothed_mls)

[update-docs]

Thanks for submitting this pull request! The maintainers of this repository would appreciate if you could update the CHANGELOG.md based on your changes.