As AI applications such as autonomous driving systems are getting more and more popular, there is an increasing need for the technique of detecting 3D objects. It is not straightforward to detect 3D objects accurately in real time by using existing object detection algorithms that use image data. Although there are also networks using LiDAR data as well as image data, they do not bear autonomous driving in terms of speed. This paper presents a light-weight multi-view neural network using multiple types of sensors such as cameras and LiDARs. An evaluation using a benchmark for autonomous driving systems demonstrated that the presented network performs faster than existing 3D object detection algorithms without degradation of detection accuracy.
Real-Time 3D Object Detection and Tracking is required for autonomous driving car and real-time systems. By detecting and tracking obstacles in 3D space, Robots can do trajectory exploration and action judgement safely. The purpose is to create high performance and accuracy algorithms by using end-to-end deep neural networks and GPU.
Parallelization of Convolutional Neural Networks (CNNs) has been considerably studied in recent years. A case study of parallelized CNNs using general-purpose computing on GPUs (GPGPU) and Message Passing Interface (MPI) has been published. On the other hand, little effort is being expended on studying scalability of parallelized CNNs on multi-core CPUs. We explores performance of the training process of CNNs achieved by increasing the number of computing cores and threads. Detailed experiments were conducted on state-of-the-art multi-core processors using OpenMP and MPI frameworks to demonstrate that Caffe-based CNNs are successfully accelerated due to well-designed multi-threaded programs. We also discussed better way to exhibit performance of multi-threaded CNNs by comparing three different implementations.