SemARFlow：为自动驾驶的无监督光流估计注入语义信息
SemARFlow: Injecting Semantics into Unsupervised Optical Flow Estimation for Autonomous Driving
AI总结：
SemARFlow通过将语义分割掩码注入编码器和可学习上采样器，并引入语义增强模块提供自监督，将KITTI-2015光流误差率从11.80%降至8.38%，同时改善边界效果和跨数据集泛化能力。
摘要：
无监督光流估计在遮挡、运动边界和低纹理区域中尤其困难。我们表明，语义和领域知识等额外信息有助于更好地约束这一问题。我们提出了SemARFlow，这是一种面向自动驾驶数据的无监督光流网络，将估计的语义分割掩码作为额外输入。这些额外信息被注入到编码器和一个用于细化光流输出的可学习上采样器中。此外，一个简单而有效的语义增强模块在学习车辆、杆状物和天空的光流及其边界时提供自监督。综合来看，这些语义信息的注入将KITTI-2015光流测试误差率从11.80%降低到8.38%。我们还展示了在物体边界周围的可见改进，以及更强的跨数据集泛化能力。代码可在 https://github.com/duke-vision/semantic-unsup-flow-release 获取。
英文摘要：
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 is available at https://github.com/duke-vision/semantic-unsup-flow-release.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
