# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
###### Abstract  
Monocular 3D object detection is a challenging task in autonomous systems due to the lack of explicit depth information in single-view images. Existing methods often depend on external depth estimators or expensive sensors, which increase computational complexity and complicate integration into existing systems. To overcome these limitations, we propose AuxDepthNet, an efficient framework for real-time monocular 3D object detection that eliminates the reliance on external depth maps or pre-trained depth models. AuxDepthNet introduces two key components: the Auxiliary Depth Feature (ADF) module, which implicitly learns depth-sensitive features to improve spatial reasoning and computational efficiency, and the Depth Position Mapping (DPM) module, which embeds depth positional information directly into the detection process to enable accurate object localization and 3D bounding box regression. Leveraging the DepthFusion Transformer (DFT) architecture, AuxDepthNet globally integrates visual and depth-sensitive features through depth-guided interactions, ensuring robust and efficient detection. Extensive experiments on the KITTI dataset show that AuxDepthNet achieves state-of-the-art performance, with \(AP_{3D}\) scores of 24.72% (Easy), 18.63% (Moderate), and 15.31% (Hard), and \(AP_{BEV}\) scores of 34.11% (Easy), 25.18% (Moderate), and 21.90% (Hard) at an IoU threshold of 0.7.  
monocular 3D object detection; depth feature learning; vehicle detection; detection algorithms 2020 IEEE/RSJ International Conference on, Vol., 2020, pp. 221-222. IEEE. Personal notice on all other things is permitted. Permission from IEEE must be obtained for all other uses, in fact, be found at: _[[http://ieee.mit.edu/~zhangr592017/0](http://ieee.mit.edu/~zhangr592017/0)]([http://ieee.mit.edu/~zhangr592017/0](http://ieee.mit.edu/~zhangr592017/0))models to generate pseudo-depth maps, which are combined with LiDAR-based 3D detectors for object recognition and localization [9; 10]. This approach, as exemplified by methods like Pseudo-LiDAR and F-Pointnet [5; 11; 12], achieves improved localization but suffers from inaccuracies in depth priors and high computational costs due to the reliance on external depth estimators, limiting their applicability in real-time scenarios. The second category focuses on feature fusion, where features from monocular images and estimated depth maps are extracted and combined to enhance detection. Methods like \(D^{4}LCN\) and CaDDN [13; 14], as shown in Figure 1b, use specialized convolutional architectures to integrate visual and depth features. While these methods demonstrate promising results, their reliance on the quality of depth maps and the complexity of their architectures can hinder efficiency and robustness in dynamic environments. To address these challenges, we propose AuxDepthNet, a novel framework for real-time monocular 3D object detection that avoids the need for external depth estimators or pre-generated depth maps. AuxDepthNet introduces two key modules: the Auxiliary Depth Feature Module (ADF) and the Depth Position Mapping Module (DPM), as depicted in Figure 1c. These modules enable implicit learning of depth-sensitive features and inject depth positional cues directly into the detection pipeline. Conventional CNN-based monocular 3D detection frameworks often struggle to capture global dependencies and integrate multi-source depth features. In contrast, Transformers leverage self-attention mechanisms to process image and depth information simultaneously [15; 16], allowing for end-to-end 3D object detection within a streamlined pipeline [17]. Built upon the DepthFusion Transformer architecture, our method efficiently integrates contextual and depth-sensitive features, achieving superior detection performance with reduced computational costs [11; 12]. The main contributions of this paper are as follows:  
1. We propose AuxDepthNet, a novel framework for efficient, real-time monocular 3D object detection that eliminates reliance on external depth maps or estimators. 2.