Abstract
Unsupervised optical flow estimation is especially hard near occlusions and motion boundaries and in low-texture regions. We show that additional information such as semantics and domain knowledge can help better constrain this problem. We introduce SemARFlow, an unsupervised optical flow network designed for autonomous driving data that takes estimated semantic segmentation masks as additional inputs. This additional information is injected into the encoder and into a learned upsampler that refines the flow output. In addition, a simple yet effective semantic augmentation module provides self-supervision when learning flow and its boundaries for vehicles, poles, and sky. Together, these injections of semantic information improve the KITTI-2015 optical flow test error rate from 11.80% to 8.38%. We also show visible improvements around object boundaries as well as a greater ability to generalize across datasets. Code will be made available.
Abstract (translated)
无监督光学流估计在遮挡和运动边界以及低纹理区域特别困难。我们证明了额外的信息,如语义和领域知识,可以帮助更好地限制这个问题。我们介绍了SemARFlow,这是一个为自主驾驶数据设计的无监督光学流网络,使用估计的语义分割掩膜作为额外的输入。这些额外的信息被注入到编码器和一个通过学习的扩展器,以优化流输出。此外,一个简单的但有效的语义增强模块在学习流和其边界对车辆、丘陵和天空时提供自我监督。通过这些注入的语义信息,KITTI-2015光学流测试错误率从11.80%降低到8.38%。我们还展示了围绕物体边界的可见改善以及更广泛地应用于数据集的能力。代码将公开提供。
URL
https://arxiv.org/abs/2303.06209
