Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection 数据集链接:https://github.com/yuliangguo/3D_Lane_Synthetic_Dataset 代码链接:https://github.com/yuliangguo/Pytorch_Generalized_3D_Lane_Detection 也是和3D-LaneNet进行对比,论文提出了一种通用且可扩展的方法,称为Gen LaneNet,用于从单...
Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection 数据集链接:https://github.com/yuliangguo/3D_Lane_Synthetic_Dataset 代码链接:https://github.com/yuliangguo/Pytorch_Generalized_3D_Lane_Detection 也是和3D-La...
代码地址:https://github.com/yuliangguo/Pytorch_Generalized_3D_Lane_Detection 本文提出了一种通用且可扩展的3D车道线检测方法,称为Gen-LaneNet。该方法的灵感来自《3d-lanenet: end-to-end 3d multiple lane detection.》中的3D LaneNet技术,同时,该方法是一个统一的框架,可在单个网络中解决图像编码、特征的空间...
代码链接:https://github.com/yuliangguo/Pytorch_Generalized_3D_Lane_Detection 也是和3D-LaneNet进行对比,论文提出了一种通用且可扩展的方法,称为Gen LaneNet,用于从单个图像中检测3D车道。该方法受到最新的3DLaneNet启发,是一个统一的框架,可在单个网络中解决图像编码、特征空间转换和3D车道预测。Gen LaneNet提出了...
代码地址:https://github.com/yuliangguo/Pytorch_Generalized_3D_Lane_Detection 本文提出了一种通用且可扩展的3D车道线检测方法,称为Gen-LaneNet。该方法的灵感来自《3d-lanenet: end-to-end 3d multiple lane detection.》中的3D LaneNet技术,同时,该方法是一个统一的框架,可在单个网络中解决图像...
In this paper, a generalized two-stage network called Att-Gen-LaneNet was proposed to achieve robust 3D lane detection in complex traffic scenes. The Efficient Channel Attention (ECA) module and the Convolutional Block Attention Module (CBAM) were combined in this network. In the first stage of...
Step 1: Revise your data path and save path in './scripts/config_3dlanenet_apollo_ws.yaml'.Step 2: Train the WS-3D-Lane network.CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 --master_port 9999 train_ws3dlane.py --cfg scripts/config_3dlanenet_apollo_...
This library is inspired byOpenLane,GenLaneNet,mmdetection3d,SparseInst,ONCEand many other related works, we thank them for sharing the code and datasets. Citation If you find LATR is useful for your research, please consider citing the paper: ...
网站地址在once-3dlanes.github.io。 大多数现有的基于图像的车道线检测方法都专注于对车道检测问题描述为2D任务,其中典型的流水线首先基于语义分割或坐标回归在图像平面中检测车道线,然后通过假设地面平坦在俯视图中投影检测车道线。利用标定良好的摄像头外参,逆透视映射(IPM)能够在平坦的地平面上获得可接受的3-D车道...
提到这类方法,不得不提它的开山之作——LaneNet!论文地址:arxiv.org/pdf/1802.0559github地址:github.com/MaybeShewill 作者用用共享的Encoder模型,设计了两个Decoder分支:车道线分割分支和车道embedding分支。前者对像素进行二分类,输出哪些pixels是车道线,哪些是背景,使用标准的交叉熵损失函数;后者输出不同的车道实例,...