# Stereo 3D Object Detection Using a Feature Attention Module
## 1 Introduction  
The advent of cutting-edge technologies like autonomous driving and robotic applications has ignited a fervent interest among researchers in intelligent object detection and localization. Networks trained on point cloud and image data aim to predict crucial object information such as position, size, and rotation angle, typically visualized through three-dimensional bounding boxes, as depicted in Figure 1. While lidar offers precise point clouds and is commonly employed for 3D object detection, its limitations in adverse weather conditions and its high cost have spurred exploration into alternative sensor forms and harnessing image information. Image-based methodologies predominantly utilize monocular and binocular cameras, accomplishing 3D object detection through depth estimations or key point detection. Binocular vision, relying on the principles of disparity estimation and triangulation, surpasses monocular images by offering depth information independent of real depth supervision, thereby enhancing detection accuracy. Its interpretability, stability, and adaptability have  
Figure 1: 3D object detection results. captivated researchers' attention. However, inherent errors between the principle-based point cloud accuracy and real values lead to estimation inaccuracies in positional information, creating a performance gap in 3D object detection compared to lidar-based systems. Closing this gap and optimizing the usability of camera sensors holds significant importance in advancing 3D vision methodologies. This pursuit carries high research and application value, particularly in domains like autonomous driving and intelligent robotics. Yet, binocular vision poses the following practical challenges in 3D object detection:  
1. Without the aid of lidar, stereo 3D object detection suffers performance setbacks, requiring retraining or augmentation with lidar for \"new\" object categories or forms, escalating equipment costs. The use of binocular vision as a cost-effective alternative necessitates addressing accuracy challenges. 2. While 2D segmentation methods from images enhance detection [1; 2], their efficacy dwindles for diverse objects like height limit devices [3], which can assume various forms (e.g., single pole, multipole, irregular natural obstacles). Exploring alternative solutions becomes imperative for enhancing detection efficacy. Additionally, point clouds for depth estimation can suffer from artifacts and deformations, lacking the precision of lidar. Researchers are diligently seeking improved solutions for enhancing 3D object detection accuracy based on stereo vision. This paper focuses on refining 3D object detection through stereo vision, harnessing its innate correspondence between stereo point clouds and images for seamless feature fusion. This fusion enhances the interpretability of stereo data, which is pivotal in improving 3D object detection accuracy. The proposed feature attention module quantifies feature importance, which could aid in the design of feature fusion methodologies. This study delved into both local and global feature fusion levels, categorizing methodologies into 2D-3D (local) and 3D (global) approaches. The primary contributions of this work are as follows:  
1. Our proposed feature attention module aims to facilitate feature fusion by evaluating the significance of stereo point cloud and image features across diverse models. This module effectively analyzes feature weights, offering targeted insights for model enhancements. 2. Utilizing our feature attention module, we scrutinized the relevance of stereo point cloud and image features within the local area of the PatchNet [4] model. This analysis sheds light on the impact of image segmentation methods. 3. Employing the feature attention module, we delved into the importance of point cloud and image features within the local realm of the Pseudo-Lidar FpointNet [5; 6] model. Additionally, we devised a category-based feature reweighting module to address declining foreground-background segmentation accuracy.