The estimation of implicit cross-frame correspondences and the high
computational cost have long been major challenges in video semantic
segmentation (VSS) for driving scenes. Prior works utilize keyframes, feature
propagation, or cross-frame attention to address these issues. By contrast, we
are the first to harness vanishing point (VP) priors for more effective
segmentation. Intuitively, objects near VPs (i.e., away from the vehicle) are
less discernible. Moreover, they tend to move radially away from the VP over
time in the usual case of a forward-facing camera, a straight road, and linear
forward motion of the vehicle. Our novel, efficient network for VSS, named
VPSeg, incorporates two modules that utilize exactly this pair of static and
dynamic VP priors: sparse-to-dense feature mining (DenseVP) and VP-guided
motion fusion (MotionVP). MotionVP employs VP-guided motion estimation to
establish explicit correspondences across frames and help attend to the most
relevant features from neighboring frames, while DenseVP enhances weak dynamic
features in distant regions around VPs. These modules operate within a
context-detail framework, which separates contextual features from
high-resolution local features at different input resolutions to reduce
computational costs. Contextual and local features are integrated through
contextualized motion attention (CMA) for the final prediction. Extensive
experiments on two popular driving segmentation benchmarks, Cityscapes and
ACDC, demonstrate that VPSeg outperforms previous SOTA methods, with only
modest computational overhead.

利用消失点先验知识的 VPSeg 模型在驾驶场景下进行视频语义分割，通过两个模块（DenseVP 和 MotionVP）利用静态和动态消失点先验知识，实现了更加有效的分割结果。这种模型在两个流行的驾驶分割基准数据集 Cityscapes 和 ACDC 上的实验证明了其在准确性方面的卓越表现，同时计算开销相对较小。