Abstract
In this paper, we propose a novel approach to address the problem of camera and radar sensor fusion for 3D object detection in autonomous vehicle perception systems. Our approach builds on recent advances in deep learning and leverages the strengths of both sensors to improve object detection performance. Precisely, we extract 2D features from camera images using a state-of-the-art deep learning architecture and then apply a novel Cross-Domain Spatial Matching (CDSM) transformation method to convert these features into 3D space. We then fuse them with extracted radar data using a complementary fusion strategy to produce a final 3D object representation. To demonstrate the effectiveness of our approach, we evaluate it on the NuScenes dataset. We compare our approach to both single-sensor performance and current state-of-the-art fusion methods. Our results show that the proposed approach achieves superior performance over single-sensor solutions and could directly compete with other top-level fusion methods.
Abstract (translated)
在本文中，我们提出了一种新的方法来解决自动驾驶感知系统中3D物体检测的问题。我们的方法基于最近在深度学习方面的进展，并利用两个传感器的优势来提高物体检测性能。具体来说，我们使用最先进的深度学习架构提取相机图像的2D特征，然后应用一种新颖的跨域空间匹配（CDSM）变换方法将它们转换为3D空间。接着，我们使用互补的融合策略将提取的雷达数据与2D特征融合，产生最终的3D物体表示。为了证明我们方法的有效性，我们在 nuScenes 数据集上进行了评估。我们将我们的方法与单传感器性能和当前最先进的融合方法进行了比较。我们的结果表明，与单传感器解决方案相比，所提出的方法具有卓越的性能，并可以直接与其他顶级融合方法竞争。
URL
https://arxiv.org/abs/2404.16548
