RUHSNet: 3D Object Detection Using Lidar Data in Real Time

Understand

In this work, we address the problem of 3D object detection from point cloud data in real time.

• For autonomous vehicles to work, it is very important for the perception component to detect the real world objects with both high accuracy and fast inference.
• We propose a novel neural network architecture along with the training and optimization details for detecting 3D objects in point cloud data.
• We compare the results with different backbone architectures including the standard ones like VGG, ResNet, Inception with our backbone.