将ARN嵌入到现有框架SC-Depth中可以实现端到端训练，并在多个数据集上大幅超过现有算法。作者：Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Tat-Jun Chin, Chunhua Shen, Ian Reid代码链接：https://github.com/JiawangBian/sc_depth_pl文章链接：https://arxiv.org/abs/2006.02708v2 一、研究背景 无监督单...
Fig. 1 SC_Depth 框架 如Fig.1 所示,我们定义了两个网络即 Depth CNN 与 Pose CNN ,他们分别用于预测输入的深度和相邻帧的位姿(需要指出的是,Fig.1 中的两个 Depth CNN 是共享参数的,在实现上是同一个网络,这一点新手比较容易产生误解)。在得到相邻帧Ia,Ib的深度图Da,Db和他们的位姿变换Pab后,通过位姿...
In the SC-DepthV2 (TPMAI 2022), we prove that the large relative rotational motions in the hand-held camera captured videos is the main challenge for unsupervised monocular depth estimation in indoor scenes. Based on this findings, we propose auto-recitify network (ARN) to handle the large ...
The predicted depth is sufficiently accurate and consistent for use in the ORB-SLAM2 system. The below video showcases the estimated depth in the form of pointcloud (top) and color map (bottom right). In the SC-DepthV2 (TPMAI 2022), we prove that the large relative rotational motions ...
针对这一问题,我们从理论上分析了相机运动与深度估计的关系,并提出一个数据预处理的方法进行实验验证。最后我们提出自校准网络(ARN)来解决复杂的相机旋转,并将其嵌入到SC-Depth中实现端到端训练。最终我们的算法(SC-DepthV2)在多个数据集上大幅超过现有算法。
In the SC-DepthV2 (TPMAI 2022), we prove that the large relative rotational motions in the hand-held camera captured videos is the main challenge for unsupervised monocular depth estimation in indoor scenes. Based on this findings, we propose auto-recitify network (ARN) to handle the large ...
In the SC-DepthV2 (TPMAI 2022), we prove that the large relative rotational motions in the hand-held camera captured videos is the main challenge for unsupervised monocular depth estimation in indoor scenes. Based on this findings, we propose auto-recitify network (ARN) to handle the large ...
In the SC-DepthV2 (TPMAI 2022), we prove that the large relative rotational motions in the hand-held camera captured videos is the main challenge for unsupervised monocular depth estimation in indoor scenes. Based on this findings, we propose auto-recitify network (ARN) to handle the large ...
In the SC-DepthV2 (TPMAI 2022), we prove that the large relative rotational motions in the hand-held camera captured videos is the main challenge for unsupervised monocular depth estimation in indoor scenes. Based on this findings, we propose auto-recitify network (ARN) to handle the large ...
In the SC-DepthV2 (TPMAI 2022), we prove that the large relative rotational motions in the hand-held camera captured videos is the main challenge for unsupervised monocular depth estimation in indoor scenes. Based on this findings, we propose auto-recitify network (ARN) to handle the large ...