解读:Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras Abstract 提出了一种仅利用相邻视频帧的一致性作为监控信号,从视频中同时估计场景深度、相机自运动、物体运动和相机内参的新方法。与之前的工作类似,我们的方法通过学习将可区分的变形应用于帧和对比结果与相邻帧,该工作...
odometry, and demonstrate qualitatively that depth prediction can be learned from a collection of YouTube videos. The code will be open sourced once anonymity is lifted. 机译:我们提出了一种新颖的方法,用于同时学习单眼视频的深度,自我运动,物体运动和相机内在特性,仅使用相邻视频帧之间的一致性作...
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论文Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras 主要有几个亮点: 1,处理移动物体时 instance segmentation and tracking are not required,不需要实例分割, 虽然文章里说还是需要一个网络预测可能移动的区域,但比起需要实例分割,难度还是下降了点。 2,occlusion-aware ...
Angelova, “Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras,” in Int. Conf. Comput. Vis., 2019. [17] V. Guizilini, R. Hou, J. Li, R. Ambrus, and A. Gaidon, “Semantically-guided representation learning for self-supervised monocular depth,”...
Depth from Videos in the Wild:Unsupervised Monocular Depth Learning from Unknown Cameras 1.引言 当信息不足以解决歧义时,深层网络可以通过从以前的例子中归纳出深度图和流场。当这个方向的研究得到了牵制[50,12,14,37,25,38],很明显物体运动是一个主要障碍,因为它违反了场景是静态的假设。为了解决这个问题,已...
Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras. In ICCV, 2019. [27] Xiaodong Gu, Zhiwen Fan, Siyu Zhu, Zuozhuo Dai, Feitong Tan, and Ping Tan. Cascade cost volume for high-resolution multi-view stereo and stereo matc...
Although the training processes of the unsupervised and semi-supervised methods rely on monocular videos or stereo image pairs, the trained depth networks predict depth maps from single images during the testing. We summarize the existing methods from the aspect of their training data, supervised ...
Depth from Videos in the Wild:Unsupervised Monocular Depth Learning from Unknown Cameras 1.引言 当信息不足以解决歧义时,深层网络可以通过从以前的例子中归纳出深度图和流场。当这个方向的研究得到了牵制[50,12,14,37,25,38],很明显物体运动是一个主要障碍,因为它违反了场景是静态的假设。为了解决这个问题,...
Depth from Videos in the Wild:Unsupervised Monocular Depth Learning from Unknown Cameras 1.引言 当信息不足以解决歧义时,深层网络可以通过从以前的例子中归纳出深度图和流场。当这个方向的研究得到了牵制[50,12,14,37,25,38],很明显物体运动是一个主要障碍,因为它违反了场景是静态的假设。为了解决这个问题,...