使用全景环视相机的基于RDC的道路语义分割阅读感悟一、文章解决的问题二、技术方案三、创新点四、实验方案与结果五、可能存在的问题

《Restricted Deformable Convolution based Road Scene Semantic Segmentation Using Surround View Camer》

一、文章解决的问题

This paper addresses 360-degree road scene semantic segmentation using surround view cameras.使用环视相机解决360°道路语义分割问题。

二、技术方案

First, in order to address large distortion problem in the fisheye images, Restricted Deformable Convolution (RDC) is proposed for semantic segmentation, which can effectively model geometric transformations by learning the shapes of convolutional filters conditioned on the input feature map. Second, in order to obtain a large-scale training set of surround view images, a novel method called zoom augmentation is proposed to transform conventional images to fisheye images. Finally, an RDC based semantic segmentation model is built; the model is trained for real-world surround view images through a multi-task learning architecture by combining real-world images with tra

最后

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