Improve dataset card: Add paper link, task categories, and sample usage

README.md

@@ -1,4 +1,170 @@
 ---
 license: mit
+task_categories:
+- object-detection
+- image-segmentation
+- image-to-3d
+tags:
+- 6d-pose-estimation
+- 2d-3d-correspondences
 ---
-https://github.com/WangYuLin-SEU/HCCEPose
+
+# HccePose (BF) Dataset
+
+This repository contains the dataset for the paper [HccePose(BF): Predicting Front & Back Surfaces to Construct Ultra-Dense 2D-3D Correspondences for Pose Estimation](https://huggingface.co/papers/2510.10177).
+
+Code: https://github.com/WangYuLin-SEU/HCCEPose
+
+## Introduction
+HccePose represents the state-of-the-art method for 6D object pose estimation based on a single RGB image. It introduces a **Hierarchical Continuous Coordinate Encoding (HCCE)** scheme, which encodes the three coordinate components of object surface points into hierarchical continuous codes. Through this hierarchical encoding, the neural network can effectively learn the correspondence between 2D image features and 3D surface coordinates of the object.
+
+In the pose estimation process, the network trained with HCCE predicts the 3D surface coordinates of the object from a single RGB image, which are then used in a **Perspective-n-Point (PnP)** algorithm to solve for the 6D pose. Unlike traditional methods that only learn the visible front surface of objects, **HccePose(BF)** additionally learns the 3D coordinates of the back surface, thereby constructing denser 2D–3D correspondences and significantly improving pose estimation accuracy.
+
+It is noteworthy that **HccePose(BF)** not only achieves high-precision 6D pose estimation but also delivers state-of-the-art performance in 2D segmentation from a single RGB image. The continuous and hierarchical nature of HCCE enhances the network’s ability to learn accurate object masks, offering substantial advantages over existing methods.
+
+<div align="center">
+ <img src="https://github.com/WangYuLin-SEU/HCCEPose/blob/main/show_vis/fig2.jpg" width=100%>
+</div>
+
+## Features
+### 🔹 Object Preprocessing
+- Object renaming and centering  
+- Rotation symmetry calibration (8 symmetry types) based on [**KASAL**](https://github.com/WangYuLin-SEU/KASAL)  
+- Export to [**BOP format**](https://github.com/thodan/bop_toolkit)
+
+### 🔹 Training Data Preparation
+- Synthetic data generation and rendering using [**BlenderProc**](https://github.com/DLR-RM/BlenderProc)
+
+### 🔹 2D Detection
+- Label generation and model training using [**Ultralytics**](https://github.com/ultralytics)
+
+### 🔹 6D Pose Estimation
+- Preparation of **front** and **back** surface 3D coordinate labels  
+- Distributed training (DDP) implementation of **HccePose**  
+- Testing and visualization via **Dataloader**  
+- **HccePose (YOLOv11)** inference and visualization on:
+  - Single RGB images  
+  - RGB videos  
+
+## Environment Setup
+
+```bash
+apt-get update && apt-get install -y wget software-properties-common gnupg2 python3-pip
+
+apt-get update && apt-get install -y libegl1-mesa-dev libgles2-mesa-dev libx11-dev libxext-dev libxrender-dev
+
+python3 -m pip install --upgrade setuptools pip
+
+pip install torch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cu118
+
+apt-get update apt-get install pkg-config libglvnd0 libgl1 libglx0 libegl1 libgles2 libglvnd-dev libgl1-mesa-dev libegl1-mesa-dev libgles2-mesa-dev cmake curl ninja-build
+
+pip install ultralytics==8.3.70 fvcore==0.1.5.post20221221 pybind11==2.12.0 trimesh==4.2.2 ninja==1.11.1.1 kornia==0.7.2 open3d==0.19.0 transformations==2024.6.1 numpy==1.26.4 opencv-python==4.9.0.80 opencv-contrib-python==4.9.0.80
+
+pip install scipy kiwisolver matplotlib imageio pypng Cython PyOpenGL triangle glumpy Pillow vispy imgaug mathutils pyrender pytz tqdm tensorboard kasal-6d
+```
+
+## Quick Start (Sample Usage)
+
+This project provides a simple **HccePose-based** application example for the **Bin-Picking** task.  
+To reduce reproduction difficulty, both the objects (3D printed with standard white PLA material) and the camera (Xiaomi smartphone) are easily accessible devices.
+
+You can:
+- Print the sample object multiple times  
+- Randomly place the printed objects  
+- Capture photos freely using your phone  
+- Directly perform **2D detection**, **2D segmentation**, and **6D pose estimation** using the pretrained weights provided in this project  
+
+---
+
+### 📦 Example Files  
+> Please keep the folder hierarchy unchanged.
+
+| Type | Resource Link |
+|------|----------------|
+| 🎨 Object 3D Models | [models](https://huggingface.co/datasets/SEU-WYL/HccePose/tree/main/demo-bin-picking/models) |
+| 📁 YOLOv11 Weights | [yolo11](https://huggingface.co/datasets/SEU-WYL/HccePose/tree/main/demo-bin-picking/yolo11) |
+| 📂 HccePose Weights | [HccePose](https://huggingface.co/datasets/SEU-WYL/HccePose/tree/main/demo-bin-picking/HccePose) |
+| 🖼️ Test Images | [test_imgs](https://huggingface.co/datasets/SEU-WYL/HccePose/tree/main/test_imgs) |
+| 🎥 Test Videos | [test_videos](https://huggingface.co/datasets/SEU-WYL/HccePose/tree/main/test_videos) |
+
+> ⚠️ Note:  
+Files beginning with `train_` are only required for training.  
+For this **Quick Start** section, only the above test files are needed.
+
+---
+
+### ⏳ Model and Loader
+During testing, import the following modules:
+- `HccePose.tester` → Integrated testing module covering **2D detection**, **segmentation**, and **6D pose estimation**.  
+- `HccePose.bop_loader` → BOP-format dataset loader for loading object models and training data.
+
+---
+
+### 📸 Example Test
+The following image shows the experimental setup:  
+Several white 3D-printed objects are placed inside a bowl on a white table, then photographed with a mobile phone.  
+
+Example input image 👇  
+<div align="center">
+ <img src="https://github.com/WangYuLin-SEU/HCCEPose/blob/main/test_imgs/IMG_20251007_165718.jpg" width="40%">
+</div>
+
+Source image: [Example Link](https://github.com/WangYuLin-SEU/HCCEPose/blob/main/test_imgs/IMG_20251007_165718.jpg)
+
+You can directly use the following script for **6D pose estimation** and visualization:
+
+```python
+import cv2
+import numpy as np
+from HccePose.tester import Tester
+from HccePose.bop_loader import bop_dataset
+if __name__ == '__main__':
+    dataset_path = '/root/xxxxxx/demo-bin-picking'
+    bop_dataset_item = bop_dataset(dataset_path)
+    CUDA_DEVICE = '0'
+    # show_op = False
+    show_op = True
+    Tester_item = Tester(bop_dataset_item, show_op = show_op, CUDA_DEVICE=CUDA_DEVICE)
+    obj_id = 1
+    for name in ['IMG_20251007_165718']:
+        file_name = '/root/xxxxxx/test_imgs/%s.jpg'%name
+        image = cv2.cvtColor(cv2.imread(file_name), cv2.COLOR_RGB2BGR)
+        cam_K = np.array([
+            [2.83925618e+03, 0.00000000e+00, 2.02288638e+03],
+            [0.00000000e+00, 2.84037288e+03, 1.53940473e+03],
+            [0.00000000e+00, 0.00000000e+00, 1.00000000e+00],
+        ])
+        results_dict = Tester_item.perdict(cam_K, image, [obj_id],
+                                                        conf = 0.85, confidence_threshold = 0.85)
+        cv2.imwrite(file_name.replace('.jpg','_show_2d.jpg'), results_dict['show_2D_results'])
+        cv2.imwrite(file_name.replace('.jpg','_show_6d_vis0.jpg'), results_dict['show_6D_vis0'])
+        cv2.imwrite(file_name.replace('.jpg','_show_6d_vis1.jpg'), results_dict['show_6D_vis1'])
+        cv2.imwrite(file_name.replace('.jpg','_show_6d_vis2.jpg'), results_dict['show_6D_vis2'])
+    pass
+```
+
+### 🎯 Visualization Results
+
+2D Detection Result (_show_2d.jpg):
+
+<div align="center"> <img src="https://github.com/WangYuLin-SEU/HCCEPose/blob/main/show_vis/IMG_20251007_165718_show_2d.jpg" width="40%"> </div>
+
+---
+
+Network Outputs:
+
+- HCCE-based front and back surface coordinate encodings
+
+- Object mask
+
+- Decoded 3D coordinate visualizations
+
+<div align="center"> <img src="https://github.com/WangYuLin-SEU/HCCEPose/blob/main/show_vis/IMG_20251007_165718_show_6d_vis0.jpg" width="100%"> 
+<img src="https://github.com/WangYuLin-SEU/HCCEPose/blob/main/show_vis/IMG_20251007_165718_show_6d_vis1.jpg" width="100%"> </div>
+
+---
+
+## 🏆 BOP LeaderBoards
+### <img src="https://github.com/WangYuLin-SEU/HCCEPose/blob/main/show_vis/bop-6D-loc.png" width=100%>
+### <img src="https://github.com/WangYuLin-SEU/HCCEPose/blob/main/show_vis/bop-2D-seg.png" width=100%>