MapNeXt: Revisiting Training and Scaling Practices for Online Vectorized
  HD Map Construction
Toyota Li
High-Definition (HD) maps are pivotal to autopilot navigation. Integrating
the capability of lightweight HD map construction at runtime into a
self-driving system recently emerges as a promising direction. In this surge,
vision-only perception stands out, as a camera rig can still perceive the
stereo information, let alone its appealing signature of portability and
economy. The latest MapTR architecture solves the online HD map construction
task in an end-to-end fashion but its potential is yet to be explored. In this
work, we present a full-scale upgrade of MapTR and propose MapNeXt, the next
generation of HD map learning architecture, delivering major contributions from
the model training and scaling perspectives. After shedding light on the
training dynamics of MapTR and exploiting the supervision from map elements
thoroughly, MapNeXt-Tiny raises the mAP of MapTR-Tiny from 49.0% to 54.8%,
without any architectural modifications. Enjoying the fruit of map segmentation
pre-training, MapNeXt-Base further lifts the mAP up to 63.9% that has already
outperformed the prior art, a multi-modality MapTR, by 1.4% while being
$\sim1.8\times$ faster. Towards pushing the performance frontier to the next
level, we draw two conclusions on practical model scaling: increased query
favors a larger decoder network for adequate digestion; a large backbone
steadily promotes the final accuracy without bells and whistles. Building upon
these two rules of thumb, MapNeXt-Huge achieves state-of-the-art performance on
the challenging nuScenes benchmark. Specifically, we push the mapless
vision-only single-model performance to be over 78% for the first time,
exceeding the best model from existing methods by 16%.