Recent geometric methods need reliable estimates of 3D motion parameters to
procure accurate dense depth map of a complex dynamic scene from monocular
images \cite{kumar2017monocular, ranftl2016dense}. Generally, to estimate
\textbf{precise} measurements of relative 3D motion parameters and to validate
its accuracy using image data is a challenging task. In this work, we propose
an alternative approach that circumvents the 3D motion estimation requirement
to obtain a dense depth map of a dynamic scene. Given per-pixel optical flow
correspondences between two consecutive frames and, the sparse depth prior for
the reference frame, we show that, we can effectively recover the dense depth
map for the successive frames without solving for 3D motion parameters. Our
method assumes a piece-wise planar model of a dynamic scene, which undergoes
rigid transformation locally, and as-rigid-as-possible transformation globally
between two successive frames. Under our assumption, we can avoid the explicit
estimation of 3D rotation and translation to estimate scene depth. In essence,
our formulation provides an unconventional way to think and recover the dense
depth map of a complex dynamic scene which is incremental and motion free in
nature. Our proposed method does not make object level or any other high-level
prior assumption about the dynamic scene, as a result, it is applicable to a
wide range of scenarios. Experimental results on the benchmarks dataset show
the competence of our approach for multiple frames.

本文提出了一种基于光流和稀疏深度先验的方法，将动态场景模型假设为分段平面模型，计算其局部刚性变换和尽可能刚性的全局变换，无需显式估计 3D 旋转和平移即可高效恢复场景的深度密集图，适用于广泛的场景。