Update README.md

README.md

@@ -4,41 +4,80 @@ tags:
 - Autonomous Driving
 - Computer Vision
 ---
-# Dataset Tutorial
+# Open MARS Dataset
 
-### The MARS dataset follows the same structure as the NuScenes Dataset.
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/66651bd4e4be2069a695e5a1/ooi8v0KOUhWYDbqbfLkVG.jpeg)
 
-Multitraversal: each location is saved as one NuScenes object, and each traversal is one scene.
+<br/>
 
-Multiagent: the whole set is a NuScenes object, and each multi-agent encounter is one scene.
+## Welcome to the tutorial of Open MARS Dataset!
 
----
-## Initialization
+Our paper has been accepted on CVPR 2024 🎉🎉🎉
+
+Checkout our [project website](https://ai4ce.github.io/MARS/) for demo videos.
+Codes to reproduce the videos are available in `/visualize` folder of `main` branch.
+
+<br/>
+
+## Intro
+### The MARS dataset is collected with a fleet of autonomous vehicles from [MayMobility](https://maymobility.com/).
+
+Our dataset uses the same structure as the [NuScenes](https://www.nuscenes.org/nuscenes) Dataset:
+
+- Multitraversal: each location is saved as one NuScenes object, and each traversal is one scene.
+- Multiagent: the whole set is a NuScenes object, and each multiagent encounter is one scene.
 
+<br/>
+
+## Download
+Both Multiagent and Multitraversal subsets are now available for [download on huggingface](https://huggingface.co/datasets/ai4ce/MARS). 
+
+<br/>
+
+## Overview
+This tutorial explains how the NuScenes structure works in our dataset, including how you may access a scene and query its samples of sensor data.
+
+- [Devkit Initialization](#initialization)
+  - [Multitraversal](#load-multitraversal)
+  - [Multiagent](#load-multiagent)
+- [Scene](#scene)
+- [Sample](#sample)
+- [Sample Data](#sample-data)
+  - [Camera](#camera-data)
+  - [LiDAR](#lidar-data)
+  - [IMU](#imu-data)
+  - [Ego & Sensor Pose](#vehicle-and-sensor-pose)
+- [LiDAR-Image projection](#lidar-image-projection)
+
+<br/>
+
+## Initialization
 First, install `nuscenes-devkit` following NuScenes's repo tutorial, [Devkit setup section](https://github.com/nutonomy/nuscenes-devkit?tab=readme-ov-file#devkit-setup). The easiest way is install via pip:
 ```
 pip install nuscenes-devkit
 ```
 
-## Usage:
 Import NuScenes devkit:
 ```
 from nuscenes.nuscenes import NuScenes
 ```
 
-Multitraversal example: loading data of location 10:
+#### Load Multitraversal 
+loading data of location 10:
 ```
 # The "version" variable is the name of the folder holding all .json metadata tables.
 location = 10
-mars_10 = NuScenes(version='v1.0', dataroot=f'/MARS_multitraversal/{location}', verbose=True)
+nusc = NuScenes(version='v1.0', dataroot=f'/MARS_multitraversal/{location}', verbose=True)
 ```
 
-Multiagent example: loading data for the full set:
+#### Load Multiagent
+loading data for the full set:
 ```
-mars_multiagent = NuScenes(version='v1.0', dataroot=f'/MARS_multiagent', verbose=True)
+nusc = NuScenes(version='v1.0', dataroot=f'/MARS_multiagent', verbose=True)
 ```
 
----
+<br/>
+
 ## Scene
 To see all scenes in one set (one location of the Multitraversal set, or the whole Multiagent set):
 ```
@@ -76,7 +115,8 @@ Output:
 - `intersection`: location index. 
 - `err_max`: maximum time difference (in millisecond) between camera images of a same frame in this scene.
 
----
+<br/>
+
 ## Sample
 Get the first sample (frame) of one scene:
 ```
@@ -109,7 +149,8 @@ Output:
 - `data`: dict of data tokens of this sample's sensor data.
 - `anns`: empty as we do not have annotation data at this moment.
 
----
+<br/>
+
 ## Sample Data
 Our sensor names are different from NuScenes' sensor names. It is important that you use the correct name when querying sensor data. Our sensor names are:
 ```
@@ -174,7 +215,7 @@ array([[661.094568 ,   0.       , 370.6625195],
        [  0.       ,   0.       ,   1.       ]]))
 ```
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/66651bd4e4be2069a695e5a1/piuFfzzsBrzW4LKgKHxAJ.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66651bd4e4be2069a695e5a1/EBo7WeD9JV1asBfbONTym.png)
 
 ---
 ### LiDAR Data
@@ -237,8 +278,7 @@ Output:
    2.6000000e+01  7.5000000e+01]]
 ```
 
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/66651bd4e4be2069a695e5a1/gxyTJM7Y45AWE9k54Q9ur.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66651bd4e4be2069a695e5a1/ZED1ba3r7qeBzkeNQK3oq.png)
 
 
 ---
@@ -330,7 +370,8 @@ CAM_FRONT_CENTER pose:
 
 ```
 
----
+<br/>
+
 ## LiDAR-Image projection
 - Use NuScenes devkit's `render_pointcloud_in_image()` method.
 - The first variable is a sample token.
@@ -345,4 +386,8 @@ nusc.render_pointcloud_in_image(my_sample['token'],
 
 Output: 
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/66651bd4e4be2069a695e5a1/KV715ekDEgLt3CysI4R9S.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66651bd4e4be2069a695e5a1/zDrqBzfs6oV5ugVCsCQLL.png)
+
+
+
+