Update README.md

README.md

@@ -16,12 +16,12 @@ paperswithcode_id: lisu
 configs:
 - config_name: default
   data_files:
-  # - split: train
-  #   path:
-  #   - Town01/**/*.parquet
-  #   - Town03/**/*.parquet
-  #   - Town05/**/*.parquet
-  #   - Town07/**/*.parquet
+  - split: train
+    path:
+    - Town01/**/*.parquet
+    - Town03/**/*.parquet
+    - Town05/**/*.parquet
+    - Town07/**/*.parquet
   - split: test
     path:
     - Town02/**/*.parquet
@@ -47,4 +47,74 @@ dataset_info:
     dtype: float32
   - name: surf_norm_z
     dtype: float32
----
+---
+# LiSu: A Dataset for LiDAR Surface Normal Estimation
+
+LiSu provides synthetic LiDAR point clouds, each annotated with surface normal vectors.
+This dataset is generated using [CARLA](https://github.com/carla-simulator/carla) simulator, ensuring diverse environmental conditions for robust training and evaluation.
+Below is an example from LiSu, where surface normals are linearly mapped to the RGB color space for intuitive visualization:
+<div style="display: flex;">
+  <img src="https://raw.githubusercontent.com/malicd/LiSu/refs/heads/master/docs/imgs/ex1.png" style="width: 49%; margin-right: 10px;">
+  <img src="https://raw.githubusercontent.com/malicd/LiSu/refs/heads/master/docs/imgs/ex2.png" style="width: 49%;">
+</div>
+
+
+## Dataset Details
+
+### Dataset Description
+
+We generate our dataset using [CARLA](https://github.com/carla-simulator/carla), a simulation framework based on the Unreal Engine.
+Specifically, we leverage nine of CARLA's twelve pre-built maps, excluding two reserved for the CARLA Autonomous Driving Challenges and one undecorated map with low geometric detail (i.e. without buildings, sidewalks, etc.).
+These selected maps represent diverse urban and rural environments, including downtown areas, small towns, and multi-lane highways.
+For each simulation, we populated the scenes with a large number of dynamic actors, such as vehicles (cars, trucks, buses, vans, motorcycles, bicycles) and pedestrians (adults, children, police) as well as static props (barrels, garbage cans, road barriers, etc.).
+The dynamic actors exhibited realistic movement patterns, governed by the underlying physics engine and adhered to real-world traffic rules, such as driving on designated roads and obeying traffic signals.
+
+To capture realistic driving scenarios, we employ a virtual LiDAR sensor mounted atop a car operating in autopilot mode.
+The LiDAR sensor is configured to emit 64 laser beams, a 10° upper and a -30° lower field of view.
+Such a common sensor configuration strikes a balance between sparsity and density, providing a challenging yet fair evaluation environment.
+To further mimic real-world conditions, we set the maximum range to 100 meters and introduce Gaussian noise with a standard deviation of 0.02 meters to the LiDAR point cloud.
+The sensor captures data at a rate of 10Hz.
+
+CARLA's default LiDAR sensor implementation is limited to position and intensity channels.
+To enable surface normal collection, we extend CARLA's ray tracer to query surface normals at each intersection point between a ray and a mesh object.
+These surface normals are then transformed into the sensor's coordinate frame and appended to the LiDAR data.
+This requires modifications to both CARLA's C++ backend and Python frontend, adding three extra channels to store the x, y, and z components of the normal vector for each LiDAR point.
+
+For each map, we conduct eleven randomly initialized and independent simulation runs.
+A simulation is terminated early if prolonged traffic halts, such as red lights, occur.
+On average, each simulation lasts approximately 50 seconds, resulting in total of 50045 labeled frames.
+To ensure rigorous evaluation, we partition our dataset into training, validation, and testing sets.
+We assign each map to exactly one split, preventing data leakage (i.e. using the same "city" in multiple splits).
+One map is designated for validation, while the remaining eight maps are divided equally between the training and testing sets.
+This results in 25053 training, 22167 testing, and 2825 validation frames.
+
+### Dataset Sources
+<div style="display: flex;">
+  <a href="https://github.com/malicd/LiSu" target="_blank">
+    <img src="https://img.shields.io/badge/GitHub-%23121011.svg?logo=github&logoColor=white" alt="GitHub">
+  </a>
+
+  <img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/malicd/lisu" style="margin-right: 20px;">
+
+  <a href="https://arxiv.org/abs/2503.08601" target="_blank">
+    <img src="https://img.shields.io/badge/Paper-arXiv-red" alt="Paper arXiv">
+  </a>
+</div>
+
+### CARLA Pull Request
+<div style="display: flex;">
+  <a href="https://github.com/carla-simulator/carla/pull/8773" target="_blank">
+    <img alt="GitHub pull request status" src="https://img.shields.io/github/status/s/pulls/carla-simulator/carla/8773?style=flat&label=PR">
+  </a>
+</div>
+
+## Citation
+
+```
+@inproceedings{cvpr2025lisu,
+  title={{LiSu: A Dataset and Method for LiDAR Surface Normal Estimation}},
+  author={Du\v{s}an Mali\'c and Christian Fruhwirth-Reisinger and Samuel Schulter and Horst Possegger},
+  booktitle={IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)},
+  year={2025}
+} 
+```