# A Lightweight Model for 3D Point Cloud Object Detection
## 1 Introduction  
Recently, autonomous driving has attracted more and more attention. As an important part of the self-driving vehicle perception system, 3D object detection can intelligently predict the location, size, and category of key 3D objects near the vehicle and provide accurate environmental information for the vehicle. Light detection and ranging (LiDAR)-based methods [1] occupy a major position in the field of 3D object detection. However, as the precision of the scanning equipment continues to increase, the scale of the original point cloud data becomes huge. Advanced 3D detectors are often accompanied by complex network structures that require billions of floating point operations (FLOPs) and are unable to be deployed on computationally limited platforms. Sufficient model compression techniques have been proposed in computer vision tasks to solve this problem, such as network pruning [2; 3; 4], quantization [5; 6; 7], lightweight model design [8; 9; 10], and knowledge distillation [11; 12; 13]. However, there are few studies on model compression in the 3D field. This paper aims to explore an efficient 3D convolutional neural network architecture that can meet the computing range required by our edge devices.  
It is well known that the convolution layer is the most important 'time killer' in neural networks. In the above model compression technology, lightweight model design is used to improve the calculation way of the convolution layer, aiming at reducing the complexity of the model and achieving a more efficient network structure. We note the advanced lightweight models in the two-dimensional field. For example, Refs. [14; 15; 16] apply grouped point-wise convolution for the input data, reducing the dimension of the data and the number of parameters of the convolution layer; Refs. [17; 18; 19] use factorized convolution to design the lightweight model, which can effectively extract features and reduce the computational complexity of the model. Based on the above two ideas, this paper designs a novel 3D sparse convolution layer module (LW-Sconv module) as the basic module of an efficient 3D object detection network. The core part of the module consists of point-wise 3D convolution and depth-wise 3D sparse convolution. Simultaneously, transpose and reshape operations are introduced to help information flow between channels.  
In this paper, an effective lightweight 3D point cloud object detection algorithm is proposed. The framework of the algorithm is composed of a novel 3D sparse convolution layer module. In addition, the algorithm is supervised and trained by three-part knowledge distillation loss, which effectively improves the detection accuracy of the algorithm. Experimental results on two public datasets show that the required operands and parameters are significantly reduced while ensuring accuracy. The main contributions of the method proposed in this paper are summarized as follows:  
1. A novel 3D sparse convolution layer module. In order to be able to deploy the model on memory-limited devices, we design an LW-Sconv module using factorized convolution and group convolution to replace the 3D sparse convolutional layers. The aim is to reduce the complexity of the model.
2. An effective lightweight 3D object detector. Through the joint learning method, the detector is trained through three-part knowledge transfer, i.e., relation transfer, feature transfer, and output transfer. It is effective in obtaining detectors with the best detection performance.  
The rest of this paper is organized as follows: The related work is reviewed in Section 2. Then, the detailed framework of the algorithm is described in Section 3 and the algorithm is evaluated by experiments in Section 4. Section 5 ends with a summary and conclusion.