  3D scene flow characterizes how the points at the current time flow to the
next time in the 3D Euclidean space, which possesses the capacity to infer
autonomously the non-rigid motion of all objects in the scene. The previous
methods for estimating scene flow from images have limitations, which split the
holistic nature of 3D scene flow by estimating optical flow and disparity
separately. Learning 3D scene flow from point clouds also faces the
difficulties of the gap between synthesized and real data and the sparsity of
LiDAR point clouds. In this paper, the generated dense depth map is utilized to
obtain explicit 3D coordinates, which achieves direct learning of 3D scene flow
from 2D images. The stability of the predicted scene flow is improved by
introducing the dense nature of 2D pixels into the 3D space. Outliers in the
generated 3D point cloud are removed by statistical methods to weaken the
impact of noisy points on the 3D scene flow estimation task. Disparity
consistency loss is proposed to achieve more effective unsupervised learning of
3D scene flow. The proposed method of self-supervised learning of 3D scene flow
on real-world images is compared with a variety of methods for learning on the
synthesized dataset and learning on LiDAR point clouds. The comparisons of
multiple scene flow metrics are shown to demonstrate the effectiveness and
superiority of introducing pseudo-LiDAR point cloud to scene flow estimation.
