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。

MV-Map: 多视角一致性离线高精度地图生成

MV-Map: Offboard HD-Map Generation with Multi-view Consistency

Deep learning has recently achieved significant progress in trajectory
forecasting. However, the scarcity of trajectory data inhibits the data-hungry
deep-learning models from learning good representations. While mature
representation learning methods exist in computer vision and natural language
processing, these pre-training methods require large-scale data. It is hard to
replicate these approaches in trajectory forecasting due to the lack of
adequate trajectory data (e.g., 34K samples in the nuScenes dataset). To work
around the scarcity of trajectory data, we resort to another data modality
closely related to trajectories-HD-maps, which is abundantly provided in
existing datasets. In this paper, we propose PreTraM, a self-supervised
pre-training scheme via connecting trajectories and maps for trajectory
forecasting. Specifically, PreTraM consists of two parts: 1) Trajectory-Map
Contrastive Learning, where we project trajectories and maps to a shared
embedding space with cross-modal contrastive learning, and 2) Map Contrastive
Learning, where we enhance map representation with contrastive learning on
large quantities of HD-maps. On top of popular baselines such as AgentFormer
and Trajectron++, PreTraM boosts their performance by 5.5% and 6.9% relatively
in FDE-10 on the challenging nuScenes dataset. We show that PreTraM improves
data efficiency and scales well with model size.

该研究提出了一种基于预训练的深度学习模型 ——PreTraM，通过将轨迹和地图相连接来提高轨迹预测的准确性和数据利用率。PreTraM 比 AgentFormer 和 Trajectron 等基线方法在 nuScenes 数据集的 FDE-10 性能上分别提高了 5.5% 和 6.9%。