  The task of detecting 3D objects is important to various robotic
applications. The existing deep learning-based detection techniques have
achieved impressive performance. However, these techniques are limited to run
with a graphics processing unit (GPU) in a real-time environment. To achieve
real-time 3D object detection with limited computational resources for robots,
we propose an efficient detection method consisting of 3D proposal generation
and classification. The proposal generation is mainly based on point
segmentation, while the proposal classification is performed by a lightweight
convolution neural network (CNN) model. To validate our method, KITTI datasets
are utilized. The experimental results demonstrate the capability of proposed
real-time 3D object detection method from the point cloud with a competitive
performance of object recall and classification.
