PivotNet: Vectorized Pivot Learning for End-to-end HD Map Construction

Understand

Vectorized high-definition map online construction has garnered considerable attention in the field of autonomous driving research.

• Most existing approaches model changeable map elements using a fixed number of points, or predict local maps in a two-stage autoregressive manner, which may miss essential details and lead to error accumulation.
• Towards precise map element learning, we propose a simple yet effective architecture named PivotNet, which adopts unified pivot-based map representations and is formulated as a direct set prediction paradigm.
• Concretely, we first propose a novel point-to-line mask module to encode both the subordinate and geometrical point-line priors in the network.