Background:
While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure.
Objective:
In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds.
Methods:
The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module.
Results:
Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability.
Conclusion:
We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.
Object Detection And Autoencoder-Based 6d Pose Estimation For Highly Cluttered Bin Picking
