  Vectorized high-definition map (HD-map) construction, which focuses on the
perception of centimeter-level environmental information, has attracted
significant research interest in the autonomous driving community. Most
existing approaches first obtain rasterized map with the segmentation-based
pipeline and then conduct heavy post-processing for downstream-friendly
vectorization. In this paper, by delving into parameterization-based methods,
we pioneer a concise and elegant scheme that adopts unified piecewise Bezier
curve. In order to vectorize changeful map elements end-to-end, we elaborate a
simple yet effective architecture, named Piecewise Bezier HD-map Network
(BeMapNet), which is formulated as a direct set prediction paradigm and
postprocessing-free. Concretely, we first introduce a novel IPM-PE Align module
to inject 3D geometry prior into BEV features through common position encoding
in Transformer. Then a well-designed Piecewise Bezier Head is proposed to
output the details of each map element, including the coordinate of control
points and the segment number of curves. In addition, based on the
progressively restoration of Bezier curve, we also present an efficient
Point-Curve-Region Loss for supervising more robust and precise HD-map
modeling. Extensive comparisons show that our method is remarkably superior to
other existing SOTAs by 18.0 mAP at least.
