Existing 3D scene flow estimation methods provide the 3D geometry and 3D
motion of a scene and gain a lot of interest, for example in the context of
autonomous driving. These methods are traditionally based on a temporal series
of stereo images. In this paper, we propose a novel monocular 3D scene flow
estimation method, called Mono-SF. Mono-SF jointly estimates the 3D structure
and motion of the scene by combining multi-view geometry and single-view depth
information. Mono-SF considers that the scene flow should be consistent in
terms of warping the reference image in the consecutive image based on the
principles of multi-view geometry. For integrating single-view depth in a
statistical manner, a convolutional neural network, called ProbDepthNet, is
proposed. ProbDepthNet estimates pixel-wise depth distributions from a single
image rather than single depth values. Additionally, as part of ProbDepthNet, a
novel recalibration technique for regression problems is proposed to ensure
well-calibrated distributions. Our experiments show that Mono-SF outperforms
state-of-the-art monocular baselines and ablation studies support the Mono-SF
approach and ProbDepthNet design.

本文提出了一种称为 Mono-SF 的新型单目 3D 场景流估计方法，将多视角几何和单视深度信息相结合，使用统计方式的卷积神经网络 ProbDepthNet 来整合单视深度估计，并通过回校准技术提高估计深度的准确性。实验证明 Mono-SF 具有优于现有单目基线的性能，对 Mono-SF 方法和 ProbDepthNet 设计的削弱研究成果支持该方法。