| --- |
| 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> |
