We investigate a challenging task of nighttime optical flow, which suffers from weakened texture and amplified noise. These degradations weaken discriminative visual features, thus causing invalid motion feature matching. Typically, existing methods employ domain adaptation to transfer knowledge from auxiliary domain to nighttime domain in either input visual space or output motion space. However, this direct adaptation is ineffective, since there exists a large domain gap due to the intrinsic heterogeneous nature of the feature representations between auxiliary and nighttime domains. To overcome this issue, we explore a common-latent space as the intermediate bridge to reinforce the feature alignment between auxiliary and nighttime domains. In this work, we exploit two auxiliary daytime and event domains, and propose a novel common appearance-boundary adaptation framework for nighttime optical flow. In appearance adaptation, we employ the intrinsic image decomposition to embed the auxiliary daytime image and the nighttime image into a reflectance-aligned common space. We discover that motion distributions of the two reflectance maps are very similar, benefiting us to consistently transfer motion appearance knowledge from daytime to nighttime domain. In boundary adaptation, we theoretically derive the motion correlation formula between nighttime image and accumulated events within a spatiotemporal gradient-aligned common space. We figure out that the correlation of the two spatiotemporal gradient maps shares significant discrepancy, benefitting us to contrastively transfer boundary knowledge from event to nighttime domain. Moreover, appearance adaptation and boundary adaptation are complementary to each other, since they could jointly transfer global motion and local boundary knowledge to the nighttime domain.

翻译：我们研究了夜间光流这一具有挑战性的任务，其面临纹理弱化和噪声放大的问题。这些退化削弱了判别性视觉特征，从而导致无效的运动特征匹配。通常，现有方法采用域适应技术，在输入视觉空间或输出运动空间中将知识从辅助域迁移到夜间域。然而，这种直接适应效果不佳，因为辅助域与夜间域之间由于特征表示的内在异质性而存在较大的域差距。为解决这一问题，我们探索了一个共同潜在空间作为中间桥梁，以增强辅助域与夜间域之间的特征对齐。本文利用白昼和事件两个辅助域，提出了一种新颖的共现边界适应框架用于夜间光流。在表观适应中，我们利用内在图像分解将辅助白昼图像和夜间图像嵌入到一个反射率对齐的共空间。我们发现两个反射率图的运动分布非常相似，这有助于我们将运动表观知识从白昼域一致地迁移到夜间域。在边界适应中，我们理论推导了在时空梯度对齐的共空间内夜间图像与累积事件之间的运动相关公式。我们发现两个时空梯度图的相关性存在显著差异，这有助于我们将边界知识从事件域对比性地迁移到夜间域。此外，表观适应与边界适应相互补充，因为它们能共同将全局运动和局部边界知识迁移到夜间域。