论文标题
单骨：单眼场景的稀疏到密集的组合方法
MonoComb: A Sparse-to-Dense Combination Approach for Monocular Scene Flow
论文作者
论文摘要
与使用更多多样化和更多传感器的汽车应用程序的持续趋势相反，该工作试图解决单眼相机设置下的复杂场景流问题，即使用单个传感器。为此，我们利用了单像深度估计，光流和稀疏到密度的插值来利用最新成就，并提出一种单眼组合方法（单本击）来计算密集的场景流。单本尸体使用光流随着时间的推移与重建的3D位置相关联，并插入遮挡区域。这样，现有的单眼方法在动态的前景区域中的表现优于，这在挑战性的Kitti 2015场景流基准基准中导致竞争对手的第二好的结果。
Contrary to the ongoing trend in automotive applications towards usage of more diverse and more sensors, this work tries to solve the complex scene flow problem under a monocular camera setup, i.e. using a single sensor. Towards this end, we exploit the latest achievements in single image depth estimation, optical flow, and sparse-to-dense interpolation and propose a monocular combination approach (MonoComb) to compute dense scene flow. MonoComb uses optical flow to relate reconstructed 3D positions over time and interpolates occluded areas. This way, existing monocular methods are outperformed in dynamic foreground regions which leads to the second best result among the competitors on the challenging KITTI 2015 scene flow benchmark.
