Cam2BEV:考虑到多个车载摄像头的图像,TensorFlow实现用于计算语义分割的鸟瞰图(BEV)图像

上传者: 42118701 | 上传时间: 2022-09-22 09:44:37 | 文件大小: 6.74MB | 文件类型: ZIP
凸轮2BEV 该存储库包含我们的方法的官方实现,该方法用于在语义上分割的鸟瞰图(BEV)图像的计算中,给出了多个车载摄像机的图像,如本文所述: 一种Sim2Real深度学习方法,用于将图像从多个车载摄像头转换为鸟瞰视图中的语义分割图像( , ) , 和 摘要—准确的环境感知对于自动驾驶至关重要。 当使用单眼相机时,环境中元素的距离估计带来了重大挑战。 将相机透视图转换为鸟瞰图(BEV)时,可以更轻松地估算距离。 对于平坦表面,反透视贴图(IPM)可以将图像准确地转换为BEV。 这种转换会使三维物体(如车辆和易受伤害的道路使用者)变形,从而使得很难估计它们相对于传感器的位置。 本文介绍了一种方法,该方法可从多个车载摄像机获得的图像中获得校正后的360°BEV图像。 校正后的BEV图像被分割成语义类别,并且包括对遮挡区域的预测。 神经网络方法不依赖人工标记的数据,而是在合成数据集

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