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!
[link] [comments]
