论文标题
多传感器3D对象检测的深连续融合
Deep Continuous Fusion for Multi-Sensor 3D Object Detection
论文作者
论文摘要
在本文中，我们提出了一个新型的3D对象检测器，该检测器可以利用LiDAR和相机来执行非常准确的定位。为了实现这一目标，我们设计了一个端到端的可学习体系结构，该体系结构可利用连续的卷积以在不同级别的分辨率下融合图像和LIDAR特征图。我们提出的连续融合层编码离散状态图像特征以及连续的几何信息。这使我们能够根据多个传感器设计一种新颖，可靠和高效的端到端可学习的3D对象检测器。我们对KITTI以及大规模3D对象检测基准的实验评估都显示出对最新技术的显着改善。
In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. Our proposed continuous fusion layer encode both discrete-state image features as well as continuous geometric information. This enables us to design a novel, reliable and efficient end-to-end learnable 3D object detector based on multiple sensors. Our experimental evaluation on both KITTI as well as a large scale 3D object detection benchmark shows significant improvements over the state of the art.
