Update README.md

README.md

@@ -20,11 +20,11 @@ GS2E addresses this gap by synthesizing event data from sparse, static RGB image
 
 The dataset includes:
 
-* **21 multi-view event sequences** across **7 scenes** and **3 difficulty levels** (easy/medium/hard)
+* **21 distinct scenes**, each with **3** corresponding event sequences under **varying blur levels** (slight, medium, and severe)
+<!-- * **21 multi-view event sequences** across **7 scenes** and **3 blur levels** (slight/medium/severe) -->
 * Per-frame photorealistic RGB renderings (clean and motion-blurred)
 * Ground truth camera poses
 * Geometry-consistent synthetic event streams
-* Consistent intrinsics and camera paths with NeRF Synthetic Dataset
 
 The result is a simulation-friendly yet physically-informed dataset for training and evaluating event-based 3D reconstruction, localization, SLAM, and novel view synthesis.
 
@@ -37,9 +37,24 @@ If you use this synthetic event dataset for your work, please cite:
 
 ## Dataset Structure and Contents
 
-This synthetic event dataset is organized first by scene, then by level of difficulty. Each sequence recording is given in the form of a [ROS bag](http://wiki.ros.org/rosbag) named `esim.bag`, with the following data streams:
+This synthetic event dataset is organized by scene, with each scene directory containing synchronized multimodal data for RGB-event processing tasks. The data was derived from MVImgNet and processed via GS2E to generate high-quality event streams. Each scene includes the following elements:
 
-| ROS Topic | Data | Publishing Rate (Hz) |
+| Path / File                  | Data Type                 | Description                                          |
+|-----------------------------|---------------------------|------------------------------------------------------|
+| `images/`                   | RGB image sequence        | Sharp, high-resolution ground truth RGB frames       |
+| `images_blur_<level>/`      | Blurred RGB image sequence| Images with different degrees of artificial blur     |
+| `sparse/`                   | COLMAP sparse model       | Contains `cameras.txt`, `images.txt`, `points3D.txt` |
+| `events.h5`                 | Event data (HDF5)         | Compressed event stream as (t, x, y, p)              |
+
+- The `events.h5` file stores events in the format:  
+  `[timestamp (μs), x (px), y (px), polarity (1/0)]`
+- `images_blur_<level>/` folders indicate increasing blur intensity.
+- `sparse/` is generated by COLMAP and includes camera intrinsics and poses.
+
+This structure enables joint processing of visual and event data for various tasks such as event-based deblurring, video reconstruction, and hybrid SfM pipelines.
+
+
+<!-- | ROS Topic | Data | Publishing Rate (Hz) |
 | :--- | :--- | :--- |
 | `/cam0/events` | Events | - |
 | `/cam0/pose` | Camera Pose | 1000 |
@@ -52,7 +67,7 @@ This synthetic event dataset is organized first by scene, then by level of diffi
 It is obtained by running the improved ESIM with the associated `esim.conf` configuration file, which references camera intrinsics configuration files `pinhole_mono_nodistort_f={1111, 1250}.yaml` and camera trajectory CSV files `{hemisphere, sphere}_spiral-rev=4[...].csv`. 
 
 The validation and test views of each scene are given in the `views/` folder, which is structured according to the NeRF synthetic dataset (except for the depth and normal maps). These views are rendered from the scene Blend-files, given in the `scenes/` folder. Specifically, we create a [Conda](https://docs.conda.io/en/latest/) environment with [Blender as a Python module](https://docs.blender.org/api/current/info_advanced_blender_as_bpy.html) installed, according to [these instructions](https://github.com/wengflow/rpg_esim#blender), to run the `bpy_render_views.py` Python script for rendering the evaluation views.
-
+ -->
 
 ## Setup