论文标题
无监督的相机姿势学习构图重新估计
Unsupervised Learning of Camera Pose with Compositional Re-estimation
论文作者
论文摘要
我们考虑了无监督的相机姿势估计的问题。给定输入视频序列，我们的目标是估计连续帧之间的相机姿势（即相机运动）。传统上，通过将严格的限制放在转换向量或通过复杂管道结合光流来解决此问题。我们提出了一种替代方法，该方法利用摄像头姿势估计的组成重新估计过程。给定输入，我们首先估计深度图。然后，我们的方法迭代根据估计的深度图估算摄像机运动。我们的方法在定量和视觉上都显着改善了预测的相机运动。此外，重新估计以一种新颖而简单的方式解决了无界类像素的问题。我们方法的另一个优点是它适用于其他相机姿势估计方法。对KITTI基准数据集的实验分析表明，我们的方法在无监督的相机移动估计中优于现有的最新方法。
We consider the problem of unsupervised camera pose estimation. Given an input video sequence, our goal is to estimate the camera pose (i.e. the camera motion) between consecutive frames. Traditionally, this problem is tackled by placing strict constraints on the transformation vector or by incorporating optical flow through a complex pipeline. We propose an alternative approach that utilizes a compositional re-estimation process for camera pose estimation. Given an input, we first estimate a depth map. Our method then iteratively estimates the camera motion based on the estimated depth map. Our approach significantly improves the predicted camera motion both quantitatively and visually. Furthermore, the re-estimation resolves the problem of out-of-boundaries pixels in a novel and simple way. Another advantage of our approach is that it is adaptable to other camera pose estimation approaches. Experimental analysis on KITTI benchmark dataset demonstrates that our method outperforms existing state-of-the-art approaches in unsupervised camera ego-motion estimation.
