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Systems and methods for semi-supervised training using reprojected distance loss

机译:使用reproeted距离损失的半监督培训的系统和方法

摘要

System, methods, and other embodiments described herein relate to training a depth model for monocular depth estimation. In one embodiment, a method includes generating, as part of training the depth model according to a supervised training stage, a depth map from a first image of a pair of training images using the depth model. The pair of training images are separate frames depicting a scene from a monocular video. The method includes generating a transformation from the first image and a second image of the pair using a pose model. The method includes computing a supervised loss based, at least in part, on reprojecting the depth map and training depth data onto an image space of the second image according to at least the transformation. The method includes updating the depth model and the pose model according to at least the supervised loss.
机译:本文描述的系统,方法和其他实施例涉及训练用于单眼深度估计的深度模型。 在一个实施例中,一种方法包括根据监督训练阶段训练深度模型的一部分,该方法包括使用深度模型的来自一对训练图像的第一图像的深度图。 这对训练图像是描绘来自单眼视频的场景的单独帧。 该方法包括使用姿势模型生成来自第一图像的变换和该对的第二图像。 该方法包括至少部分地基于对深度映射和训练深度数据来计算在根据至少变换的图像空间上的监督损耗。 该方法包括根据至少监督损耗更新深度模型和姿势模型。

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