This work investigates the estimation of dense three-dimensional motion fields, commonly referred to as scene flow. While great progress has been made in recent years, large displacements and adverse imaging conditions as observed in natural outdoor environments are still very challenging for current approaches to reconstruction and motion estimation. In this paper, we propose a unified random field model which reasons jointly about 3D scene flow as well as the location, shape and motion of vehicles in the observed scene. We formulate the problem as the task of decomposing the scene into a small number of rigidly moving objects sharing the same motion parameters. Thus, our formulation effectively introduces long-range spatial dependencies which commonly employed local rigidity priors are lacking. Our inference algorithm then estimates the association of image segments and object hypotheses together with their three-dimensional shape and motion. We demonstrate the potential of the proposed approach by introducing a novel challenging scene flow benchmark which allows for a thorough comparison of the proposed scene flow approach with respect to various baseline models. In contrast to previous benchmarks, our evaluation is the first to provide stereo and optical flow ground truth for dynamic real-world urban scenes at large scale. Our experiments reveal that rigid motion segmentation can be utilized as an effective regularizer for the scene flow problem, improving upon existing two-frame scene flow methods. At the same time, our method yields plausible object segmentations without requiring an explicitly trained recognition model for a specific object class.

这项工作研究了密集的三维运动场（通常称为场景流）的估计。尽管近年来已经取得了长足的进步，但是对于当前的重建和运动估计方法，在自然室外环境中观察到的大位移和不利的成像条件仍然非常具有挑战性。在本文中，我们提出了一个统一的随机场模型，该模型共同考虑3D场景流以及观察场景中车辆的位置，形状和运动。我们将问题表述为将场景分解为少量共享相同运动参数的刚性移动对象的任务。因此，我们的公式有效地引入了通常缺乏局部刚性的远距离空间依赖性。然后，我们的推理算法会估计图像段和对象假设的关联以及它们的三维形状和运动。我们通过引入一种新颖的具有挑战性的场景流基准测试来演示该方法的潜力，该基准可以针对各种基准模型对建议的场景流方法进行全面比较。与以前的基准相比，我们的评估是第一个为大规模动态真实世界城市场景提供立体声和光流地面真实性的评估。我们的实验表明，刚性运动分割可以用作场景流问题的有效正则化方法，并改进了现有的两帧场景流方法。同时，我们的方法可以产生合理的对象分割，而无需为特定的对象类别进行经过明确训练的识别模型。