  Reliable segmentation of road lines and markings is critical to autonomous
driving. Our work is motivated by the observations that road lines and markings
are (1) frequently occluded in the presence of moving vehicles, shadow, and
glare and (2) highly structured with low intra-class shape variance and overall
high appearance consistency. To solve these issues, we propose a Homography
Guided Fusion (HomoFusion) module to exploit temporally-adjacent video frames
for complementary cues facilitating the correct classification of the partially
occluded road lines or markings. To reduce computational complexity, a novel
surface normal estimator is proposed to establish spatial correspondences
between the sampled frames, allowing the HomoFusion module to perform a
pixel-to-pixel attention mechanism in updating the representation of the
occluded road lines or markings. Experiments on ApolloScape, a large-scale lane
mark segmentation dataset, and ApolloScape Night with artificial simulated
night-time road conditions, demonstrate that our method outperforms other
existing SOTA lane mark segmentation models with less than 9\% of their
parameters and computational complexity. We show that exploiting available
camera intrinsic data and ground plane assumption for cross-frame
correspondence can lead to a light-weight network with significantly improved
performances in speed and accuracy. We also prove the versatility of our
HomoFusion approach by applying it to the problem of water puddle segmentation
and achieving SOTA performance.
