Improve dataset card: Add task category, paper, code, project page links, and refined sample usage

README.md

@@ -1,20 +1,23 @@
 ---
+license: mit
+task_categories:
+- image-to-3d
 configs:
 - config_name: doors
-  data_files: "doors/*.json"
+  data_files: doors/*.json
 - config_name: stairs
-  data_files: "stairs/*/*.ply"
+  data_files: stairs/*/*.ply
 - config_name: windows
-  data_files: "windows/*.json"
+  data_files: windows/*.json
 - config_name: poses
-  data_files: "poses/*.json"
+  data_files: poses/*.json
 - config_name: structures
-  data_files: "structures/*.obj"
-license: mit
+  data_files: structures/*.obj
 ---
 
 # HouseLayout3D: A Benchmark Dataset for 3D Layout Estimation in the Wild
 
+[Paper](https://huggingface.co/papers/2512.02450) | [Project Page](https://houselayout3d.github.io) | [Code](https://github.com/HouseLayout3D/houselayout3d)
 
 **HouseLayout3D** is a challenging benchmark dataset for **3D layout estimation in large-scale, multi-floor buildings**. It is built upon real-world building scans from [Matterport3D](https://niessner.github.io/Matterport/), and provides detailed annotations of structural elements across up to five floors and forty rooms per building. The dataset is designed to support research in scene understanding, indoor mapping, and robotics applications that require vectorized, object-free representations of indoor spaces.
 
@@ -33,39 +36,47 @@ Most existing datasets and models for 3D layout estimation are tailored for smal
 - Annotations of windows and doors (including opening direction) as 3D rectangles.
 - Annotations of stairs as 3D polygons.
 
-## Getting Started
+## Sample Usage
 
-Follow these steps to set up the environment and visualize a scene from the dataset.
+Follow these steps to set up the environment, download the dataset, and visualize a scene.
 
 ### 1. Installation
 
-```bash
-# Clone the evaluation repo
-git clone https://github.com/valebi/house-layout-3d-eval.git
-cd house-layout-3d-eval
+First, clone the main repository and install dependencies:
 
-# Create and activate a conda environment
-conda create -n house-layout3d python=3.9 -y
-conda activate house-layout3d
+```bash
+conda create --name houselayout3d python=3.10 -y
+conda activate houselayout3d
 
-# Install dependencies
+git clone https://github.com/HouseLayout3D/houselayout3d.git
+cd houselayout3d
 pip install -r requirements.txt
+
+# Install Git LFS to download large files
+# On Mac
+brew install git-lfs
+# On Linux
+sudo apt-get install git-lfs
 ```
 
-### 2. Run the Visualizer
+### 2. Download the Dataset
 
-First, download the dataset and place it in your desired `DATASET_ROOT` directory.
+Download the HouseLayout3D dataset using `git lfs` from Hugging Face:
 
 ```bash
-git clone https://huggingface.co/datasets/houselayout3d/HouseLayout3D ./data
+git lfs install # Ensure lfs is initialized
+git clone https://huggingface.co/datasets/houselayout3d/HouseLayout3D data
 ```
 
-Then run the visualization script with:
+### 3. Visualize a Scene
+
+Navigate to the `houselayout3d` directory (where you cloned the code in step 1) and run the visualization script:
 
 ```bash
-python visualize.py --dataset-root DATASET_ROOT --scene JmbYfDe2QKZ
+python visualize.py
+python -m http.server 6008
 ```
-
+Then open your browser and navigate to `http://localhost:6008` to view the visualizations.
 
 ## Data Structure
 ```text