Improve dataset card: Add image-to-3d task, update paper link, and enhance usage guide

README.md

@@ -1,32 +1,46 @@
 ---
 license: cc-by-4.0
-tags:
-- computer-vision,
-- inverse-rendering,
-- photometric-stereo,
-- computer-graphics,
-- display,
-- polarization,
-- stereo,
-- multi-light,
-- illumination-multiplexing,
-pretty_name: Display Inverse Rendering Dataset
 size_categories:
 - n<1K
+pretty_name: Display Inverse Rendering Dataset
+tags:
+- computer-vision
+- inverse-rendering
+- photometric-stereo
+- computer-graphics
+- display
+- polarization
+- stereo
+- multi-light
+- illumination-multiplexing
+task_categories:
+- image-to-3d
 papers:
-  - title: "A Real-world Display Inverse Rendering Dataset"
-    url: "https://michaelcsj.github.io/DIR/"
-homepage: "https://michaelcsj.github.io/DIR/"
-repository: "https://github.com/MichaelCSJ/DIR"
+- title: A Real-world Display Inverse Rendering Dataset
+  url: https://huggingface.co/papers/2508.14411
+homepage: https://michaelcsj.github.io/DIR/
+repository: https://github.com/MichaelCSJ/DIR
 ---
+
 # Display Inverse Rendering Dataset
-- 📄 [Paper (ArXiv)]()  
+- 📄 [Paper](https://huggingface.co/papers/2508.14411)
 - 🌐 [Project Page](https://michaelcsj.github.io/DIR/)
 - 💻 [GitHub Repository](https://github.com/MichaelCSJ/DIR)
-  
+
 ## Introduction
 This dataset is created for display inverse rendering, including multi-light stereo images captured by polarization cameras, and GT geometry (pixel-aligned point cloud and surface normals) scanned by high-precision 3D scanner.
 
+`DIR dataset` is a dataset for `Display Inverse Rendering (DIR)`. It contains assets captured from an LCD & polarization-camera system.
+*   **OLAT Images:** are captured under display superpixels, and can be used to simulate arbitrary display patterns.
+*   **GT Geometry:** is scanned with a high-precision 3D scanner.
+*   **Lighting information:** We carefully calibrated light direction, non-linearity, and backlight.
+*   **Stereo Imaging:** is an optional feature to initialize rough geometry.
+
+**Why Display Inverse Rendering?**
+Display inverse rendering uses a monitor as a per-pixel, programmable light source to reconstruct object geometry and reflectance from captured images. Key features include:
+*   **Illumination Multiplexing:** encodes multiple lights and reduces demanded a number of inputs.
+*   **Leveraging Polarization:** enables diffuse-specular separation based on optics.
+
 ## Structure
 - DIR-basic: The basic version of the dataset released with the paper. It includes stereo polarized RAW images, RGB images from a reference view, and ground-truth surface normals and point clouds. All images are captured under a multi-light configuration projected through 16×9 superpixels on the display.
 
@@ -59,7 +73,61 @@ This dataset is created for display inverse rendering, including multi-light ste
 ```
 
 - DIR-pms: This dataset follows the DiLiGeNT format and has the same composition as **DIR-basic**. It provides multi-light RGB images from the reference view along with related information and the ground-truth normal maps.
-- 
+-
+```
+  ├── A [Suffix (default "PNG")]
+  │  ├──'000 - 143.png',
+  │  ├──'filenames.txt',
+  │  ├──'light_directions.txt'
+  │  ├──'light_intensities.txt',
+  │  ├──'mask.png'
+  │  ├──'Normal_gt.mat'
+```
+
+## Getting Started
+
+### ⚙️ Installation
+
+```bash
+git clone https://github.com/MichaelCSJ/DIR.git
+cd DIR
+conda env create -f environment.yml
+conda activate DIR
+```
+
+### 🗂️ Dataset Preparation
+Download the [DIR dataset](https://huggingface.co/datasets/SeokjunChoi/display-inverse-rendering-dataset) for perform our display inverse rendering baseline. It consists of 16 real-world objects with diverse shapes and materials under precisely calibrated directional lighting. There are some versions of dataset as **'DIR-basic'**, **'DIR-pms'**, **'DIR-hdr'**, and **'DIR-multi-distance'**.
+*   **DIR-basic**: The basic version of the dataset released with the paper. It includes stereo polarized RAW images, RGB images from a reference view, and ground-truth surface normals and point clouds. All images are captured under a multi-light configuration projected through 16×9 superpixels on the display.
+```
+  ├── A
+  │  ├──GT_geometry (for reference(main) view)
+  │  │  ├──'normal.npy',
+  │  │  ├──'normal.png',
+  │  │  ├──'point_cloud_gt.npy'
+  │  ├──main
+  │  │  ├──diffuseNspecular
+  │  │  │  ├──'000 - 143.png',
+  │  │  │  ├──'black.png',
+  │  │  │  ├──'white.png',
+  │  │  ├──RAW_polar
+  │  │  │  ├──'000 - 143_[SHUTTER_TIME(us)].png',
+  │  │  │  ├──'black_[SHUTTER_TIME(us)].png',
+  │  │  │  ├──'white_[SHUTTER_TIME(us)].png',
+  │  ├──side
+  │  │  ├──diffuseNspecular
+  │  │  │  ├──'000 - 143.png',
+  │  │  │  ├──'black.png',
+  │  │  │  ├──'white.png',
+  │  │  ├──RAW_polar
+  │  │  │  ├──'000 - 143_[SHUTTER_TIME(us)].png',
+  │  │  │  ├──'black_[SHUTTER_TIME(us)].png',
+  │  │  │  ├──'white_[SHUTTER_TIME(us)].png',
+  │  ├──'mask.png'
+  │  ├──'point_cloud.npy' (unprojected pixel w.r.t. depth & focal length)
+```
+
+*   **DIR-pms**: This dataset follows the [DiLiGenT](https://sites.google.com/site/photometricstereodata/single) format and has the same composition as **DIR-basic**. It provides multi-light RGB images from the reference view along with related information and the ground-truth normal maps.
+
 ```
   ├── A [Suffix (default "PNG")]
   │  ├──'000 - 143.png',
@@ -70,8 +138,61 @@ This dataset is created for display inverse rendering, including multi-light ste
   │  ├──'Normal_gt.mat'
 ```
 
+*   **DIR-hdr**: TBD.
+*   **DIR-multi-distance**: TBD.
+
+After downloading, place them under `data/` as the following directory tree.
+
+### 🔥 Normal and basis BRDF Recovery
+To run the baseline, execute `train.py` with the following command:
+```bash
+python train.py --name YOUR_SESSION_NAME --dataset_root YOUR_DATASET_PATH
+```
+By default, this code performs inverse rendering using multi-light images captured with an OLAT pattern.
+If you want to use a small number of multi-light images with a multiplexed display pattern, run the code as follows:
+```bash
+python train.py --name YOUR_SESSION_NAME --dataset_root YOUR_DATASET_PATH --use_multiplexing True --initial_light_pattern YOUR_DISPLAY_PATTERNS
+```
+You can use [display patterns](https://github.com/MichaelCSJ/DIR/tree/main/patterns) provided by `DDPS` for `YOUR_DISPLAY_PATTERNS`.
+Place display patterns under `patterns/` as the following directory tree.
+
+**Lighting Patterns (Initial)**:
+<p align="center">
+  <img src="https://github.com/MichaelCSJ/DIR/blob/main/assets/learned_illum_initial.png?raw=true" width="400px">
+</p>
+
+**Lighting Patterns (Learned)**:
+<p align="center">
+  <img src="https://github.com/MichaelCSJ/DIR/blob/main/assets/learned_illum_optimized.png?raw=true" width="400px">
+</p>
+
+Once training is completed, a folder named `YYYYMMDD_HHMMSS` will be created inside the `/results/SESSION` directory, containing the TensorBoard logs, OLAT rendering results, and the fitted parameters for each object.
+
+### 🖼️ Novel Relighting (Optional)
+Run `relighting.py` to render images under novel directional lightings based on recovered normal map and BRDF parameter maps.
+To output .avi video:
+```bash
+python relighting.py --datadir ./results/YOUR_SESSION_NAME/OBJECT_NAME --format avi
+```
+
+## Citation
+
+If you find this repository useful, please consider citing this paper:
+
+```bibtex
+@inproceedings{choi2025realworld,
+      title={A Real-world Display Inverse Rendering Dataset},
+      author={Seokjun Choi and Hoon-Gyu Chung and Yujin Jeon and Giljoo Nam and Seung-Hwan Baek},
+      booktitle={IEEE/CVF International Conference on Computer Vision (ICCV)},
+      year={2025},
+      url={https://huggingface.co/papers/2508.14411}
+}
+```
+
 ## TODO
 - [x] ~~Release training code.~~
 - [x] ~~Release `Display Inverse Rendering (DIR)` dataset.~~
 - [ ] Release EXPANDED version of DIR datset (HDR).
-- [ ] Release EXPANDED version of DIR datset (multi-distance).
+- [ ] Release EXPANDED version of DIR datset (multi-distance).
+- [ ] Release additional visualization tools.
+- [ ] Release raw image processing code.