Duplicate from YasiiKB/R3PM-Net

Co-authored-by: Yasaman Kashefbahrami <YasiiKB@users.noreply.huggingface.co>

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README.md

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+---
+pretty_name: Sioux-Cranfield, Sioux-Scans Datasets
+task_categories:
+- feature-extraction
+tags:
+- 3D
+- registration
+- CAD
+- computer_vision
+- point_clouds
+---
+# R3PM-Net Datasets
+This repository contains the two proposed datasets in [R3PM-Net paper](arxiv.org/abs/2604.05060); **Sioux-Cranfield** and **Sioux-Scans**, which aim to address the gap between synthetic datasets and real-world industrial data.
+It also contains the pickle files made from a subset of the Sioux-Cranfield dataset that can be used to train models.
+
+## Folder Structure
+```
+R3PM-Net/
+├── README.md
+├── down_sampled_modelnet40.zip
+├── simulators.zip
+├── sioux_cranfield.zip
+└── sioux_scans.zip
+```
+
+## Downsampled ModelNet40
+To save time, we provide a downsampled version of ModelNet40 test set. All the point clouds are downsampled to 2000 points.
+
+## Simulators
+This directory contains pickle (.pkl) files compatible with the [Learning3d](https://github.com/vinits5/learning3d) library and can be used to train or fine-tune models. 
+These files are created from a subset of the Sioux-Cranfield containing the "teeth", "cube", "lime" and "lego" CAD models. There are 320 point cloud pairs in total, with 80-20 train-test split. 
+
+## Sioux-Cranfield
+This is a diverse collection of 13 objects designed to evaluate model robustness across varying data qualities. The dataset contains 4
+computer-aided design (CAD) models generated via photogrammetric reconstruction, 3 synthetic CAD models, and 6 pristine geometries from the [Cranfield Benchmark](https://github.com/Menthy-Denayer/PCR_CAD_Model_Alignment_Comparison/tree/main/datasets). This combination allows for a comprehensive evaluation of performance on both high-quality synthetic meshes and realistically imperfect reconstructions.
+
+### Composition of the Sioux-Cranfield Dataset
+This table provides a structured breakdown of the composition of this dataset.
+
+<div align="center">
+  
+| **Category** | **Source Type** | **Qty** |
+| :--- | :--- | :--- |
+| Sioux (Reconstructed) | Photogram. | 4 |
+| Sioux (Synthetic) | CAD Models | 3 |
+| Cranfield [1] | Pristine | 6 |
+| **Total** | **---** | **13** |
+
+*Sioux (Reconstructed) and Sioux (Synthetic) are also used in Sioux-Scans dataset to produce Target point clouds.*
+<br>
+</div>
+
+<div align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/65e05d75e7ab5ac3383cc2b9/y_99Q386K3CszAW48LNHJ.png" width="60%">
+  <p><b>CAD models of the Sioux-Cranfield dataset.</b>The first six belong to the Cranfield Assembly benchmark and the rest are contributions of this paper (Sioux dataset).</p>
+</div>
+
+## Sioux-Scans 
+This dataset addresses the real-world challenge of registering physical scans to digital models. 
+The targets are CAD models of seven small objects (shared with Sioux-Cranfield), while the sources are raw event-camera scans of the corresponding objects acquired via the custom Quality Control [Sioux 3DoP setup](https://brainporteindhoven.com/fileadmin/user_upload/TechMarkt/Advanced_Manufacturing/3DOP_Presentation_WP4.pdf). 
+To generate these scans, the setup utilizes a laser beam and an event-based camera to produce accurate point clouds from moving or handheld objects. 
+Unlike traditional frame-based sensors, this camera captures discrete brightness changes as the laser sweeps across the surface, resulting in highly precise point clouds. 
+Before processing, gross outliers were filtered. However, these data represent a substantially more challenging setting than synthetic benchmarks, as they reflect inevitable deficiencies, such as sparsity, noise, and occlusions, rarely present in ideal simulated datasets. 
+These artifacts stem from sensor noise, lighting sensitivity, and viewpoint-dependent gaps, particularly on sharp edges or reflective surfaces.
+
+<div align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/65e05d75e7ab5ac3383cc2b9/OrlERYP5aQW0Wf4Nsog3L.png" width="60%">
+  <p><b>Sioux-Scans point cloud data.</b> Target (blue) and Source (yellow) point clouds for seven distinct objects.</p>
+</div>

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