# 3D Object Detection Using Frustums and Attention Modules for Images and Point Clouds
## 1 Introduction  
The detection of object instances in 3D sensory data has tremendous importance in many applications. Three-dimensional (3D) technology can receive more abundant and comprehensive environmental information. Therefore, it is widely used in robot navigation, automatic driving, Augmented Reality (AR), and industrial detection. Point cloud and RGB image fusion can simultaneously extract 2D and 3D features by using a neural network. Objects can be detected with higher accuracy by simultaneously considering 2D and 3D information. With the progress of point clouds [1, 2], 3D object detection methods [3, 4] can resort to learning features directly from point clouds. For example, PointNets [3, 4] are capable of classifying a whole point cloud or predicting a semantic class for each point in the point clouds. Three-dimensional (3D) point clouds are usually transformed into images or voxel grids [5] before PointNet [3, 4]. It shows good performance in 3D object detection. However, the weakness of PointNet [3, 4] and PointNet++ [4] is that a 3D bounding box cannot be estimated with direction. A new Frustum scheme was proposed by F-PointNets [2] and Frustum ConvNet [1], which use RGB-D data and a multilayer 2D region proposal to help the point clouds' segmentation form the 3D space. The global features are obtained from the local feature combination. F-PointNets used T-net [2] to determine the position and direction of a 3D bounding box. The disadvantage of the Frustum is that objects with unclear boundaries and small-scale instances are difficult to detect. To solve this problem, we would like to refer to the attention modules used in 2D object detection methods. [PERSON] et al. [5] proposed a method based on a Gaussian Mixture Model (GMM), in which attention modules were improved by colour, intensity, and orientation feature maps. The attention modules focused on interesting areas to enhance the features of the edge information and small objects. [PERSON] et al. [6] proposed a Region Proposal Network (RPN) with an attention module, enabling the detector to pay attention to objects with high resolution while perceiving the surroundings with low resolution. These works inspired us to use attention modules for object detection in 3D point clouds. In our work, we developed the images and 3D point clouds fusion method to improve 3D object detection. A new attention module was designed with Frustum ConvNet [1] to enhance feature extraction and improve small object detection. We added attention modules to the input layer of Multilayer Perceptron (MLP) in PointNet [3,4]. We used the tanh activation function to extract and strengthen the attention of a small object, which can improve small object detection effectively. In this paper, we propose a Frustum ConvNet with attention modules for 3D object detection, in which both images and point clouds are used. The contributions of the paper are as follows:  
1. In the PointNet of Frustum ConvNet, we added the Convolutional Block Attention Module (CBAM) [7] attention module at the hidden layer of Multilayer Perceptron (MLP) to improve the detection accuracy. The CBAM attention module can improve the contrast between the object and the surrounding environment. 2. We propose an improved attention module by adding Multilayer Perceptron (MLP) and using the tanh activation function. The tanh function is used for average-pooling and max-pooling layers to extract features. The mean of the tanh activation function is 0. Furthermore, the tanh function can cope with cases when the feature values have big differences. Finally, the feature information of the pooling layers is fused through the sigmoid function. 3. We evaluated our approach on the KITTI [8] object detection benchmark.