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.

我们研究了夜间光流的一项具有挑战性的任务，该任务在纹理衰弱和噪声放大等问题下受到影响。我们探索了一个共同的潜在空间作为中间桥梁，以加强辅助领域和夜间领域之间的特征对齐，提出了一种新颖的共同外观 - 边界自适应框架用于夜间光流。我们利用内在图像分解将辅助白天图像和夜间图像嵌入到一个反射对齐的共同空间中进行外观自适应，发现两个反射图像的运动分布非常相似，从而有助于将运动外观知识一致地从白天领域传递到夜间领域。在边界自适应中，我们从一个时空梯度对齐的共同空间中理论推导了夜间图像和累积事件之间的运动相关公式，发现两个时空梯度图的相关性存在显著差异，从而有利于将边界知识从事件领域对比性地传递到夜间领域。此外，外观自适应和边界自适应相互补充，因为它们可以共同将全局运动和局部边界知识传递到夜间领域。