Update README with IPAN_3D reference

README.md

@@ -1,89 +1,101 @@
-# Granulo-10k
+<div align="center">
 
-<p align="center">
-  <b>A large-scale benchmark dataset for multiple-view industrial granulometry of OSB wood strands.</b>
-</p>
+# 🌲 Granulo-10k
 
-<p align="center">
-  <a href="#dataset">Dataset</a> •
-  <a href="#download">Download</a> •
-  <a href="#tasks">Tasks</a> •
-  <a href="#baselines">Baselines</a> •
-  <a href="#citation">Citation</a>
-</p>
+### A large-scale benchmark dataset for multiple-view industrial granulometry of OSB wood strands
+
+[![Dataset](https://img.shields.io/badge/Dataset-Hugging%20Face-yellow?logo=huggingface)](https://huggingface.co/datasets/AngeloUNIMI/Granulo-10k)
+[![GitHub](https://img.shields.io/badge/GitHub-Granulo--10k-181717?logo=github)](https://github.com/AngeloUNIMI/Granulo-10k)
+[![Related Code](https://img.shields.io/badge/Related-IPAN__3D-blue?logo=github)](https://github.com/AngeloUNIMI/IPAN_3D)
+[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](LICENSE)
+[![Task](https://img.shields.io/badge/Task-Industrial%203D%20Granulometry-green)](#tasks)
+
+**Granulo-10k** is an open benchmark dataset for research on **Oriented Strand Board (OSB) strand analysis**, with a focus on **multiple-view granulometry**, **strand segmentation**, and **3D geometric estimation**.
+
+</div>
 
 ---
 
-## Overview
+## 🧭 Overview
 
-**Granulo-10k** is an open benchmark dataset for research on **Oriented Strand Board (OSB) strand analysis**, with a focus on multiple-view granulometry and three-dimensional geometric estimation.
+Granulo-10k contains high-resolution paired images of OSB wood strands acquired with a calibrated two-camera setup, together with segmentation masks, granulometric ground truth, and 3D point clouds.
 
-The dataset contains high-resolution paired images of wood strands acquired with a calibrated two-camera setup, together with segmentation masks, granulometric ground truth, and 3D point clouds. It is designed to support reproducible research on automated strand segmentation and estimation of:
+The dataset supports reproducible research on the estimation of:
 
 - **height** (`h`)
 - **width** (`w`)
 - **thickness** (`t`)
+- **compliant / non-compliant strand classification**
 
-Granulo-10k addresses a key limitation in industrial wood vision research: most existing approaches estimate geometry from 2D projections and often do not provide individual strand-level thickness measurements. This dataset provides multiple views and point-cloud information to encourage research on full 3D strand characterization.
+Granulo-10k addresses a key limitation in industrial wood-vision research: many existing methods rely mainly on 2D projections and do not provide individual strand-level thickness measurements. This dataset provides synchronized multiple views and point-cloud information to encourage research on full **3D strand characterization**.
 
 ---
 
-## Highlights
+## ✨ Highlights
 
-- **9,600 RGB images** at `1280 x 960` resolution
-- **200 unique OSB wood strands**
-- **24 acquisitions per strand**
-- **2 synchronized camera views** per acquisition
-- **Segmentation masks** for each paired acquisition
-- **3D point clouds** associated with each paired acquisition
-- **Ground-truth granulometric measurements**: height, width, and thickness
-- **Compliant / non-compliant strand labels** based on manufacturer reference dimensions
-- **Strand-disjoint evaluation protocol** to avoid train-test leakage
+| Feature | Description |
+|---|---|
+| 🖼️ RGB images | **9,600** images at `1280 x 960` resolution |
+| 🌲 Strands | **200** unique OSB wood strands |
+| 🔁 Acquisitions | **24** acquisitions per strand |
+| 📷 Views | **2 synchronized camera views** per acquisition |
+| 🎯 Masks | Segmentation masks for each paired acquisition |
+| ☁️ 3D data | Point clouds associated with paired acquisitions |
+| 📏 Ground truth | Height, width, and thickness measurements |
+| 🏷️ Labels | Compliant / non-compliant strand categories |
+| 🧪 Protocol | Strand-disjoint evaluation to avoid train-test leakage |
 
 ---
 
-## Dataset
-
-Granulo-10k contains images of thinly chopped wood pieces, known as **strands**, used in OSB panel production.
+## 🖼️ Visual Examples
 
-### Strand categories
+<div align="center">
 
-The dataset includes 200 manually measured strands, divided into two classes:
+![Granulo-10k example](figures/fig2_dataset_examples.png)
 
-| Category | Number of strands | Average size |
-| --- | ---: | --- |
-| Compliant | 100 | `h x w x t = 115 x 20 x 0.70 mm` |
-| Non-compliant | 100 | `h x w x t = 91 x 9 x 0.65 mm` |
+</div>
 
-For each strand, the maximum height and width were measured using a caliper. Since thickness may vary across the strand surface, multiple thickness measurements were collected at different points and averaged.
+---
 
-### Acquisition protocol
+## 🏗️ Acquisition Setup
 
 Images were collected using a calibrated multiple-view acquisition system composed of:
 
 - two synchronized **Sony SX90CR** color cameras
 - a trigger mechanism connected to a photocell
-- four LED bars arranged to provide approximately uniform illumination
+- four LED bars for approximately uniform illumination
 - calibrated camera geometry for multiple-view reconstruction
 
-The two cameras were placed at the same height and oriented at an angle of approximately `85 deg` with respect to the support, with a camera distance of `125 mm`. LED bars were placed at approximately `90 mm` from the cameras.
+The two cameras were placed at the same height and oriented at approximately `85°` with respect to the support, with a camera distance of `125 mm`. LED bars were placed at approximately `90 mm` from the cameras.
+
+<div align="center">
 
-Each strand was dropped from random positions above the cameras, while ensuring that it fell inside the intersection of the two fields of view. To increase acquisition variability, each strand was acquired 24 times:
+![Outline of the multiple-view acquisition setup](figures/setup.png)
 
-- 8 frontal drops
-- 8 sideways drops
-- 8 intermediate-orientation drops
+</div>
 
-This results in:
+Each strand was dropped from random positions above the cameras while ensuring that it fell inside the intersection of the two fields of view. To increase acquisition variability, each strand was acquired 24 times:
 
 ```text
 200 strands x 24 acquisitions x 2 cameras = 9,600 RGB images
 ```
 
-### Data modalities
+---
+
+## 📦 Dataset Content
 
 For each paired acquisition, Granulo-10k provides:
 
+```text
+Granulo-10k/
+├── Images/        # RGB images from synchronized camera views
+├── Masks/         # Segmentation masks for each view
+├── PCs/           # 3D point clouds
+└── README.md      # Dataset description and citation
+```
+
+Each acquisition includes:
+
 - RGB image from camera 1
 - RGB image from camera 2
 - segmentation mask for camera 1
@@ -94,76 +106,101 @@ For each paired acquisition, Granulo-10k provides:
 
 ---
 
-## Download
+## 🌲 Strand Categories
+
+The dataset includes 200 manually measured strands divided into two classes:
+
+| Category | Number of strands | Reference average size |
+|---|---:|---|
+| ✅ Compliant | 100 | `h x w x t = 115 x 20 x 0.70 mm` |
+| ⚠️ Non-compliant | 100 | `h x w x t = 91 x 9 x 0.65 mm` |
+
+For each strand, maximum height and width were measured using a caliper. Since thickness can vary across the strand surface, multiple thickness measurements were collected at different points and averaged.
+
+---
+
+## 📥 Download
+
+The dataset can be downloaded from Hugging Face:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("AngeloUNIMI/Granulo-10k")
+```
+
+Dataset page:
 
-Dataset can be downloaded from Hugging Face [here](https://huggingface.co/datasets/AngeloUNIMI/Granulo-10k).
+```text
+https://huggingface.co/datasets/AngeloUNIMI/Granulo-10k
+```
 
 ---
 
-## Tasks
+## 🎯 Tasks
 
 Granulo-10k supports several research tasks:
 
 ### 1. Strand segmentation
-
 Estimate binary masks for OSB strands from single-view or paired RGB images.
 
 ### 2. Multiple-view granulometry
-
-Estimate strand height, width, and thickness using one or more of the available modalities:
+Estimate strand height, width, and thickness using one or more available modalities:
 
 - image-only input
 - point-cloud-only input
 - fused image and point-cloud input
 
 ### 3. Compliant vs. non-compliant classification
-
 Classify strands according to whether their geometry is compliant with manufacturer reference dimensions.
 
 ### 4. Multi-modal geometric learning
-
-Develop methods that combine two RGB views and 3D point clouds for robust geometric reasoning.
+Develop methods that combine synchronized RGB views and 3D point clouds for robust geometric reasoning.
 
 ---
 
-## Baselines
+## 🧠 Baseline Workflow
 
 The accompanying paper evaluates modern visual backbones and multi-modal fusion strategies for joint estimation of height, width, and thickness.
 
-### Architecture summary
+```mermaid
+graph LR
+    A[Camera 1 RGB] --> B[Image Encoder]
+    C[Camera 2 RGB] --> D[Image Encoder]
+    E[3D Point Cloud] --> F[PointNet++ Encoder]
+    B --> G[Feature Fusion]
+    D --> G
+    F --> G
+    G --> H[MMoE Decoder]
+    H --> I[Height Regression]
+    H --> J[Width Regression]
+    H --> K[Thickness Regression]
+```
 
-The proposed baseline uses:
+The baseline uses:
 
 - two frozen image encoders, one for each camera view
-- a PointNet++ encoder for the point cloud
-- an MLP adapter to align point-cloud features with image embeddings
+- a PointNet++ encoder for point-cloud features
+- an MLP adapter to align point-cloud and image embeddings
 - max-pooling feature fusion
-- a Multi-gate Mixture-of-Experts (MMoE) decoder
+- a Multi-gate Mixture-of-Experts decoder
 - task-specific heads for height, width, and thickness regression
 - a multi-task uncertainty-weighted loss
 
-### Evaluation protocol
-
-Experiments use:
-
-- 5-fold cross-validation
-- 50 training epochs per fold
-- learning rate of `1.2e-3`
-- strand-disjoint splits, so all acquisitions of the same strand are assigned to the same split
-- MAE and MAPE as evaluation metrics
+---
 
-### Reported results
+## 📊 Representative Results
 
-The following table summarizes representative results from the paper. Values are reported as mean ± standard deviation.
+Values are reported as mean ± standard deviation.
 
 | Backbone | Point cloud | Height MAE [mm] | Height MAPE [%] | Width MAE [mm] | Width MAPE [%] | Thickness MAE [mm] | Thickness MAPE [%] |
-| --- | :---: | ---: | ---: | ---: | ---: | ---: | ---: |
+|---|:---:|---:|---:|---:|---:|---:|---:|
 | Mean value baseline | - | 17.88 ± 1.45 | 21.58 ± 2.86 | 6.24 ± 0.50 | 56.22 ± 3.56 | 0.18 ± 0.03 | 29.29 ± 2.32 |
 | DINO ViT-B/14 | No | 2.70 ± 0.34 | 2.99 ± 0.34 | 1.65 ± 0.21 | 12.13 ± 1.67 | 0.09 ± 0.01 | 13.70 ± 0.67 |
 | ConvNeXtV2 | No | 2.95 ± 0.31 | 3.30 ± 0.37 | 1.64 ± 0.13 | 11.67 ± 1.16 | 0.09 ± 0.01 | 14.91 ± 1.52 |
 | EVA02-CLIP ViT-L/14 | No | 2.82 ± 0.34 | 3.19 ± 0.35 | 1.73 ± 0.10 | 12.11 ± 0.70 | 0.09 ± 0.00 | 14.63 ± 1.33 |
 | CLIP ViT-L/14 | No | 2.99 ± 0.24 | 3.34 ± 0.16 | 1.84 ± 0.17 | 13.38 ± 1.69 | 0.11 ± 0.01 | 17.77 ± 1.67 |
-| DINO ViT-B/14 | Yes | **2.53 ± 0.08** | **2.77 ± 0.09** | **1.59 ± 0.02** | **11.94 ± 0.35** | 0.10 ± 0.00 | 14.58 ± 0.57 |
+| **DINO ViT-B/14** | **Yes** | **2.53 ± 0.08** | **2.77 ± 0.09** | **1.59 ± 0.02** | **11.94 ± 0.35** | 0.10 ± 0.00 | 14.58 ± 0.57 |
 | ConvNeXtV2 | Yes | 2.93 ± 0.16 | 3.24 ± 0.16 | 1.79 ± 0.06 | 13.94 ± 0.38 | 0.10 ± 0.00 | 15.13 ± 0.13 |
 | EVA02-CLIP ViT-L/14 | Yes | 3.01 ± 0.07 | 3.31 ± 0.08 | 1.78 ± 0.07 | 13.47 ± 0.93 | 0.11 ± 0.00 | 17.44 ± 0.79 |
 | CLIP ViT-L/14 | Yes | 3.41 ± 0.14 | 3.80 ± 0.12 | 2.06 ± 0.17 | 15.93 ± 2.02 | 0.13 ± 0.01 | 19.96 ± 1.19 |
@@ -172,7 +209,27 @@ DINO ViT-B/14 with point-cloud input achieves the strongest performance for heig
 
 ---
 
-## Citation
+## 🔗 Related Work: IPAN_3D
+
+Granulo-10k is closely related to the earlier work on image-processing-based 3D granulometry.
+
+The related repository provides MATLAB source code for the 2019 IEEE Transactions on Industrial Informatics paper:
+
+> **3-D granulometry using image processing**  
+> R. Donida Labati, A. Genovese, E. Muñoz, V. Piuri, and F. Scotti  
+> *IEEE Transactions on Industrial Informatics*, vol. 15, no. 3, pp. 1251-1264, March 2019.
+
+Useful links:
+
+- Code: https://github.com/AngeloUNIMI/IPAN_3D
+- Project page: http://iebil.di.unimi.it/projects/ipan
+- Paper: https://ieeexplore.ieee.org/document/8411142
+
+It can be considered a methodological precursor for multiple-view industrial granulometry, while Granulo-10k provides a larger benchmark dataset for modern learning-based methods using synchronized images, masks, point clouds, and strand-level granulometric ground truth.
+
+---
+
+## 📚 Citation
 
 If you use Granulo-10k in your research, please cite the associated paper:
 
@@ -185,12 +242,44 @@ If you use Granulo-10k in your research, please cite the associated paper:
 }
 ```
 
+If you refer to the related IPAN_3D method or code, please also cite:
+
+```bibtex
+@Article{ipan3d,
+  author  = {R. {Donida Labati} and A. Genovese and E. Mu\~{n}oz and V. Piuri and F. Scotti},
+  title   = {3-D granulometry using image processing},
+  journal = {IEEE Transactions on Industrial Informatics},
+  volume  = {15},
+  number  = {3},
+  pages   = {1251--1264},
+  month   = {March},
+  year    = {2019},
+  note    = {1551-3203}
+}
+```
+
 ---
 
-## Acknowledgements
+## 🙏 Acknowledgements
 
 This work was supported in part by the EC under project **EdgeAI** (`101097300`). Project EdgeAI is supported by the Chips Joint Undertaking and its members, including top-up funding by Austria, Belgium, France, Greece, Italy, Latvia, the Netherlands, and Norway.
 
 The authors thank **IMAL s.r.l.**, San Damaso, Modena, Italy, for cooperation in providing data and sample classification. The authors also acknowledge Prof. **Ruggero Donida Labati** for his contribution to the data collection process.
 
+---
+
+## 📄 License
+
+This repository is released under the **GNU General Public License v3.0**.
+
+See the [LICENSE](LICENSE) file for details.
+
+---
+
+<div align="center">
+
+### 🌲 Granulo-10k
+
+**Multiple-view industrial granulometry for OSB strand analysis**
 
+</div>