论文标题
rope3d:用于自动驾驶和单眼3D对象检测任务的theroadside感知数据集
Rope3D: TheRoadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task
论文作者
论文摘要
用于自动驾驶的同时感知数据集主要局限于额叶视图,并安装了车辆上的传感器。它们都不是为被忽视的路边知觉任务而设计的。另一方面,从路边摄像机捕获的数据对正面视图数据具有优势,据信这可以促进更安全,更智能的自主驾驶系统。为了加快路边感知的进步,我们从一种新颖的看法中提出了第一个高度多样性的路边感知3D数据集3D。该数据集由50k图像和超过1.5m的3D对象组成,在各种场景中,它们在不同的设置下捕获,包括具有模棱两可的安装位置,相机规格,观点和不同环境条件的各种摄像机。我们进行了严格的2d-3d关节注释和全面的数据分析,并通过指标和评估Devkit建立了新的3D路边知觉基准。此外,我们定制现有的额叶视图单眼3D对象检测方法,并建议利用几何约束,以解决由各种传感器,观点引起的固有歧义。我们的数据集可在https://thudair.baai.ac.cn/rope上找到。
Concurrent perception datasets for autonomous driving are mainly limited to frontal view with sensors mounted on the vehicle. None of them is designed for the overlooked roadside perception tasks. On the other hand, the data captured from roadside cameras have strengths over frontal-view data, which is believed to facilitate a safer and more intelligent autonomous driving system. To accelerate the progress of roadside perception, we present the first high-diversity challenging Roadside Perception 3D dataset- Rope3D from a novel view. The dataset consists of 50k images and over 1.5M 3D objects in various scenes, which are captured under different settings including various cameras with ambiguous mounting positions, camera specifications, viewpoints, and different environmental conditions. We conduct strict 2D-3D joint annotation and comprehensive data analysis, as well as set up a new 3D roadside perception benchmark with metrics and evaluation devkit. Furthermore, we tailor the existing frontal-view monocular 3D object detection approaches and propose to leverage the geometry constraint to solve the inherent ambiguities caused by various sensors, viewpoints. Our dataset is available on https://thudair.baai.ac.cn/rope.