  Real-scale scene flow estimation has become increasingly important for 3D
computer vision. Some works successfully estimate real-scale 3D scene flow with
LiDAR. However, these ubiquitous and expensive sensors are still unlikely to be
equipped widely for real application. Other works use monocular images to
estimate scene flow, but their scene flow estimations are normalized with scale
ambiguity, where additional depth or point cloud ground truth are required to
recover the real scale. Even though they perform well in 2D, these works do not
provide accurate and reliable 3D estimates. We present a deep learning
architecture on permutohedral lattice - MonoPLFlowNet. Different from all
previous works, our MonoPLFlowNet is the first work where only two consecutive
monocular images are used as input, while both depth and 3D scene flow are
estimated in real scale. Our real-scale scene flow estimation outperforms all
state-of-the-art monocular-image based works recovered to real scale by ground
truth, and is comparable to LiDAR approaches. As a by-product, our real-scale
depth estimation also outperforms other state-of-the-art works.
