While bird's-eye-view (BEV) perception models can be useful for building
high-definition maps (HD-Maps) with less human labor, their results are often
unreliable and demonstrate noticeable inconsistencies in the predicted HD-Maps
from different viewpoints. This is because BEV perception is typically set up
in an 'onboard' manner, which restricts the computation and consequently
prevents algorithms from reasoning multiple views simultaneously. This paper
overcomes these limitations and advocates a more practical 'offboard' HD-Map
generation setup that removes the computation constraints, based on the fact
that HD-Maps are commonly reusable infrastructures built offline in data
centers. To this end, we propose a novel offboard pipeline called MV-Map that
capitalizes multi-view consistency and can handle an arbitrary number of frames
with the key design of a 'region-centric' framework. In MV-Map, the target
HD-Maps are created by aggregating all the frames of onboard predictions,
weighted by the confidence scores assigned by an 'uncertainty network'. To
further enhance multi-view consistency, we augment the uncertainty network with
the global 3D structure optimized by a voxelized neural radiance field
(Voxel-NeRF). Extensive experiments on nuScenes show that our MV-Map
significantly improves the quality of HD-Maps, further highlighting the
importance of offboard methods for HD-Map generation.

本文提出了一种名为 MV-Map 的离线管道，能够有效地生成高清地图，并优化了离线管道的流程。通过聚合所有航空预测的帧，根据不确定性网络赋权重，实现了多视图一致性的增强。在 nuscenes 数据集上的实验证明了本文方法有效性的同时，也突显出将 HD-Map 生成过程转移至离线计算的重要性。