  Constructing vectorized high-definition maps from surround-view cameras has
garnered significant attention in recent years. However, the commonly employed
multi-stage sequential workflow in prevailing approaches often leads to the
loss of early-stage information, particularly in perspective-view features.
Usually, such loss is observed as an instance missing or shape mismatching in
the final birds-eye-view predictions. To address this concern, we propose a
novel approach, namely \textbf{HybriMap}, which effectively exploits clues from
hybrid features to ensure the delivery of valuable information. Specifically,
we design the Dual Enhancement Module, to enable both explicit integration and
implicit modification under the guidance of hybrid features. Additionally, the
perspective keypoints are utilized as supervision, further directing the
feature enhancement process. Extensive experiments conducted on existing
benchmarks have demonstrated the state-of-the-art performance of our proposed
approach.
