Create README.md

README.md

@@ -0,0 +1,150 @@
+# Pixel-aligned RGB-NIR Stereo Imaging and Dataset for Robot Vision
+
+> **CVPR 2025**  
+> **Jinnyeong Kim**, **Seung-Hwan Baek**  
+> POSTECH  
+> [[arXiv]](https://arxiv.org/abs/2411.18025) • [[Code]](https://github.com/your-repo-url) • [[Video]](https://your-video-link.com) • [[Dataset on HuggingFace]](https://huggingface.co/datasets/your-dataset-url)
+
+---
+
+## Overview
+
+This repository provides the code and dataset accompanying our CVPR 2025 paper:
+
+**"Pixel-aligned RGB-NIR Stereo Imaging and Dataset for Robot Vision"**
+
+We propose a novel robotic vision system equipped with **two pixel-aligned RGB-NIR stereo cameras** and a **LiDAR sensor** mounted on a mobile robot. Our system captures **RGB-NIR stereo video sequences** and **temporally synchronized LiDAR point clouds**, offering a high-quality, aligned multi-spectral dataset under diverse lighting conditions.
+
+![System Overview](https://divisonofficer.github.io/project_page_Pixel_aligned_RGB_NIR_Stereo/fig_imaging_1.png)
+
+---
+
+## ✨ Highlights
+
+- **Pixel-aligned RGB-NIR stereo imaging** for robust vision under challenging lighting.
+- **Continuous video sequences** recorded using a mobile robot.
+- **Sparse LiDAR point clouds** temporally synchronized with stereo imagery.
+- Two proposed methods to utilize RGB-NIR pairs:
+  - RGB-NIR **Image Fusion** (pretrained model-compatible)
+  - RGB-NIR **Feature Fusion** (for fine-tuned stereo depth estimation)
+
+---
+
+## 📦 Dataset
+
+We release a large-scale dataset for training and evaluating robot vision models in realistic environments.
+
+### 📹 Data Statistics
+
+| | #Videos | #Frames |
+|---|--------|---------|
+| Training | 80 | 90,000 |
+| Testing  | 40 | 7,000 |
+
+### 📁 Per Frame Data Includes:
+
+- Pixel-aligned **RGB-NIR stereo images**
+- **Sparse LiDAR** point cloud (in camera coordinates)
+- **Sensor timestamps** (synchronized)
+
+### 🌗 Lighting Scenarios
+<img width="920" alt="image" src="https://github.com/user-attachments/assets/a07bea4e-5674-4277-a585-f556ce9d4825" />
+
+
+➡️ **[Code is availabe on github](https://github.com/divisonofficer/Pixel_aligned_RGB_NIR_Stereo)**
+
+Each .tar.gz file follows below structure
+```
+frame1
+--rgb
+-----left_distorted.png (or left.png)
+-----right_distorted.png (or right.png)
+--nir
+-----left_distorted.png (or left.png)
+-----right_distorted.png (or right.png)
+storage.hdf5
+```
+The frame ids are named after their creation date.
+**_distorted.png** image need to be undistorted. **left.png** and **right.png** are undistorted version.
+
+**storage.hdf5** is H5 database. it contains **frame** group with children of each frame ids.
+
+---
+
+## 📷 Imaging System
+
+Our robotic platform integrates:
+
+- **Two RGB-NIR stereo cameras** (pixel-aligned RGB and NIR sensors)
+- **LiDAR sensor**
+- **Omnidirectional mobile base** (360° movement)
+- **High-capacity battery** (up to 6 hours)
+- **NIR LED bar light source** for consistent active illumination
+
+![Robot Platform](https://divisonofficer.github.io/project_page_Pixel_aligned_RGB_NIR_Stereo/fig_imaging_1.png)
+
+---
+
+## 🔧 Methods
+
+### RGB-NIR synthetic data augmentation
+
+![image](https://github.com/user-attachments/assets/00805f64-44cf-4ac4-927c-a01ace160f39)
+
+See **visualize/synth_aug_render.ipynb** for method of synthetic data augmentation to build RGB-NIR training dataset. 
+
+
+### RGB-NIR Image Fusion
+![image](https://github.com/user-attachments/assets/0d524c12-8419-48d0-8c3a-0b8a9bc29d1b)
+
+We introduce an RGB-NIR **image-level fusion technique** for 3-channel vision tasks. This approach allows existing **RGB-pretrained models** to benefit from NIR information **without additional fine-tuning**.
+
+Applicable to:
+- Stereo Depth Estimation
+- Semantic Segmentation
+- Object Detection
+
+See **net/image_fusion.py** for pytorch implementation.
+
+### RGB-NIR Feature Fusion (Stereo Depth)
+
+We extend RAFT-Stereo with a novel **feature-level fusion strategy**, alternating between fused and NIR **correlation volumes** during iterative disparity estimation using GRUs.
+
+![image](https://github.com/user-attachments/assets/ef954e60-02d4-4a6c-b126-150ee2edeffc)
+
+See **net/feature_fusion.py** of implementation with RAFT-Stereo as baseline
+Our setup reflects the **RGB with active illumination** scenario:  
+- NIR provides robust depth cues  
+- RGB complements NIR with texture under normal lighting
+
+---
+
+## 📊 Experimental Results
+
+Our experiments demonstrate that pixel-aligned RGB-NIR inputs:
+- Improve stereo depth accuracy under low-light and high-contrast conditions
+- Enable pretrained RGB models to generalize better
+- Enhance robustness across lighting domains
+
+
+
+
+---
+
+
+
+
+
+## 📄 Citation
+
+If you use this dataset or code, please cite our work:
+
+```bibtex
+@article{kim2025pixelnir,
+  author    = {Jinnyeong Kim and Seung-Hwan Baek},
+  title     = {Pixel-aligned RGB-NIR Stereo Imaging and Dataset for Robot Vision},
+  conference = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year      = {2025},
+  doi       = {10.48550/arXiv.2411.18025},
+  url       = {https://arxiv.org/abs/2411.18025},
+}