3D Object Detection using YOLO4 | LiDAR Dataset

This is a tutorial on how to perform 3D object detection on LiDAR Dataset.
I have used Kitti dataset in the Implementation.

Topics covered:
1- what is 3D object detection
2- Difference between 2D vs 3d Object detection
3- What is LiDAR
4- Dataset (discuss about the dataset which we are going to use in our class and how to download it)
5- Practical Implementation step by step .

For Queries: You can comment or you can write me an email at aarohisingla1987@gmail.com

2D prediction only provides 2D bounding boxes but with 3D Object detection, we can know various details of that object like size of an object, position of that object and orientation of that object. And we can use them in variety of applications in robotics, self-driving vehicles, augmented reality.
There are various 3D datasets available which are related to street scenes due to the popularity of research into self-driving cars. Those datasets are created by 3D capture sensors like LIDAR.

LiDAR use laser to measure the distance from the objects.
Lidar use Ultraviolet Visible Infrared to sense the object.

It basically checks how much time the omitted light takes to return back to the sensor and on the basis of that it gets the distance from the objects.

To run the model, you need to download and unzip the following data:

Velodyne point clouds (29 GB): Information about the surrounding for a single frame gathered by Velodyne HDL64 laser scanner. This is the primary data we use. It is 100 milliseconds snapshot of the 3d world around the car 

Left color images of object data set (12 GB): The cameras were one color camera stereo pairs. We use left Images corresponding to the velodyne point clouds for each frame.

Camera calibration matrices of object data set (16 MB): Used for calibrating and rectifying the data captured by the camera and sensor.

Training labels of object data set (5 MB).
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