论文标题
在动态场景中无监督的单眼深度学习
Unsupervised Monocular Depth Learning in Dynamic Scenes
论文作者
论文摘要
我们提出了一种共同训练深度，自我感动和相对于场景的物体密集的3D翻译场的方法，单眼光度一致性是唯一的监督来源。我们表明，这个显然不确定的问题可以通过对3D翻译字段的以下先验知识进行正规化：它们很稀疏，因为大多数场景都是静态的，并且对于刚性移动的物体而言，它们往往是恒定的。我们表明，仅此正则化就足以训练超过动态场景的先前工作中获得的精度的单眼深度预测模型，包括需要语义输入的方法。代码在https://github.com/google-research/google-research/tree/master/depth_and_motion_learning。
We present a method for jointly training the estimation of depth, ego-motion, and a dense 3D translation field of objects relative to the scene, with monocular photometric consistency being the sole source of supervision. We show that this apparently heavily underdetermined problem can be regularized by imposing the following prior knowledge about 3D translation fields: they are sparse, since most of the scene is static, and they tend to be constant for rigid moving objects. We show that this regularization alone is sufficient to train monocular depth prediction models that exceed the accuracy achieved in prior work for dynamic scenes, including methods that require semantic input. Code is at https://github.com/google-research/google-research/tree/master/depth_and_motion_learning .
