本帖最后由 keer_zu 于 2024-3-5 13:17 编辑
Previous works struggle with practicality due to their complicated spatial transformations and inflexible representations of 3D lanes.
之前工作存在的困难是：复杂的空间变换和3D车道不够灵活的表现
Faced with the issues,our work proposes an efficient and robust monocular 3Dlane detection called BEV-LaneDet with three main contributions.
针对之前的问题从三方面给出解决办法。
First, we introduce the Virtual Camera that unifiesthe in/extrinsic parameters of cameras mounted on differentvehicles to guarantee the consistency of the spatial relationship among cameras. It can effectively promote the learning procedure due to the unified visual space.
首先：
“虚拟摄像机”的引入，旨在屏蔽相机的差异。
We secondlypropose a simple but efficient 3D lane representation calledKey-Points Representation. This module is more suitable torepresent the complicated and diverse 3D lane structures.
第二：
引入叫做Key-Points（关键点）的车道表示，它更适合复杂的车道。
At last, we present a light-weight and chip-friendly spatial transformation module named Spatial TransformationPyramid to transform multiscale front-view features intoBEV features.
第三，给出了一个轻量且芯片友好的空间变换模型---空间变换金字塔，将多量程前视特征转换为BEV特征
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