Omkar2118/datasets huggingface dataset:
FAUST-partial

FAUST-partial is a 3D registration benchmark dataset created to provide a more informative evaluation of 3D registration methods. 
The dataset addresses two main limitations of current 3D registration benchmarks:

1. Lack of data variability within the current registration benchmarks
2. Capability to evaluate the registration method on a single 3D registration parameter (rotation, translation or overlap)

The benchmark is created using the FAUST training dataset comprised of 100 3D scans of human bodies. Note however, that the methodology can be applied to any point cloud dataset.

The benchmark generation for a single scan from the FAUST training dataset can be summarized as follows:

- Surround the scan with a regular icosahaedron. Each point of the icosahaedron acts as a viewpoint
- For each viewpoint, create a partial point cloud using the hidden point removal algorithm
- Finally, for a pair of partial point clouds in a desired range of overlap, generate a random rotation from the desired rotation and translation ranges.

To create a more informative 3D registration benchmark, 3 difficulty settings are used: Easy (E), Medium (M) and Hard (H) for each registration parameter: Rotation (R), Translation (T) and Overlap (O). This way, 9 benchmarks are created in which each triplet increases the difficulty of one registration parameter whilst fixing the difficulty of the other two. This allows to evaluate a 3D registration method w.r.t. only one registration parameter and observe the robustness of the method w.r.t. that parameter.

The benchmarks are denominated as FP-{R,T,O}-{E,M,H}.

Introducing paper: Addressing the generalization of 3D registration methods with a featureless baseline and an unbiased benchmark