Topological Data Analysis-friendly CAD/3D point cloud dataset [P]

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Hello everyone, I’m seeking a suitable 3D point cloud or CAD/mesh dataset for a research project focused on comparing Topological Data Analysis (TDA) with traditional preprocessing methods. The study will evaluate classification accuracy after applying various perturbations, such as Gaussian noise and random point deletion. Ideally, I need a dataset featuring classes with distinct topological structures, such as spheres versus toroidal shapes, and ample samples (600+) for robust analysis.

Hi everyone,

I’m looking for a suitable 3D point cloud dataset — or a CAD/mesh dataset from which I can sample point clouds — for a small research/report project.

The goal is to compare Topological Data Analysis (TDA) as a preprocessing / feature extraction method against more standard 3D point cloud preprocessing methods, under different perturbations such as:

Gaussian jitter / noise
random point deletion / subsampling
small deformations
scaling / rotations
outliers or other synthetic corruptions

The comparison would be based on the classification accuracy of a downstream model after preprocessing.

I do not necessarily need many classes. Even a binary classification dataset would be enough. What matters most is that the classes should differ in their topological structure, ideally in the number of holes / loops / cavities, so that TDA has a meaningful signal to detect.

For example, something like:

sphere / ball-like objects vs torus / ring-like objects
solid object vs object with a tunnel
objects with different numbers of handles or holes

Ideally, each class should contain many samples (600+), or the dataset should contain enough CAD/mesh models so that I can sample many point clouds from them.

Does anyone know of a dataset that fits this description? I would also appreciate suggestions for CAD repositories, synthetic dataset generators, or benchmark datasets where such class pairs could be extracted.

Thanks!

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