Abstract
Standard benchmarks for optical flow, scene flow, and stereo vision algorithms generally focus on model accuracy rather than robustness to image corruptions like noise or rain. Hence, the resilience of models to such real-world perturbations is largely unquantified. To address this, we present RobustSpring, a comprehensive dataset and benchmark for evaluating robustness to image corruptions for optical flow, scene flow, and stereo models. RobustSpring applies 20 different image corruptions, including noise, blur, color changes, quality degradations, and weather distortions, in a time-, stereo-, and depth-consistent manner to the high-resolution Spring dataset, creating a suite of 20,000 corrupted images that reflect challenging conditions. RobustSpring enables comparisons of model robustness via a new corruption robustness metric. Integration with the Spring benchmark enables public two-axis evaluations of both accuracy and robustness. We benchmark a curated selection of initial models, observing that accurate models are not necessarily robust and that robustness varies widely by corruption type. RobustSpring is a new computer vision benchmark that treats robustness as a first-class citizen to foster models that combine accuracy with resilience. It will be available at this https URL.
Abstract (translated)
光学流、场景流和立体视觉算法的标准基准通常侧重于模型的准确性，而不是其对诸如噪声或雨等图像退化的鲁棒性。因此，这些模型应对现实世界扰动的能力没有得到充分量化。为了解决这个问题，我们提出了RobustSpring，这是一个全面的数据集和基准测试平台，用于评估光学流、场景流和立体视觉模型在图像退化情况下的鲁棒性。RobustSpring将20种不同的图像退化（包括噪声、模糊、颜色变化、质量下降以及天气扭曲）以时间上、立体视图上及深度上的一致方式应用于高分辨率的Spring数据集，从而生成一套反映挑战条件的20,000张受损图片。RobustSpring通过一个新的鲁棒性度量指标来比较不同模型的鲁棒性，并且与Spring基准测试平台集成可以进行公开的二维评估（准确性及鲁棒性）。我们对初始的一系列精心挑选的模型进行了基准测试，发现准确性的模型不一定具有较强的鲁棒性，并且不同的退化类型表现出不同程度的鲁棒性。RobustSpring是一个新的计算机视觉基准，将鲁棒性视为第一公民，旨在促进既能保持精度又具有抗干扰能力的模型的发展。更多信息将在 [此链接](https://www.example.com) 上发布。
URL
https://arxiv.org/abs/2505.09368
