论文标题
用于3D对象检测的多任务多传感器融合
Multi-Task Multi-Sensor Fusion for 3D Object Detection
论文作者
论文摘要
在本文中，我们建议利用多个相关任务以进行准确的多传感器3D对象检测。为了实现这一目标，我们提出了一个端到端的可学习体系结构，该体系结构建议2D和3D对象检测以及地面估计和深度完成。我们的实验表明，所有这些任务都是互补的，并通过融合各个级别的信息来帮助网络学习更好的表示形式。重要的是，我们的方法在2D，3D和BEV对象检测上领导Kitti基准测试，同时是实时的。
In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. Our experiments show that all these tasks are complementary and help the network learn better representations by fusing information at various levels. Importantly, our approach leads the KITTI benchmark on 2D, 3D and BEV object detection, while being real time.
