The recent development of online static map element (a.k.a. HD Map)
construction algorithms has raised a vast demand for data with ground truth
annotations. However, available public datasets currently cannot provide
high-quality training data regarding consistency and accuracy. To this end, we
present CAMA: a vision-centric approach for Consistent and Accurate Map
Annotation. Without LiDAR inputs, our proposed framework can still generate
high-quality 3D annotations of static map elements. Specifically, the
annotation can achieve high reprojection accuracy across all surrounding
cameras and is spatial-temporal consistent across the whole sequence. We apply
our proposed framework to the popular nuScenes dataset to provide efficient and
highly accurate annotations. Compared with the original nuScenes static map
element, models trained with annotations from CAMA achieve lower reprojection
errors (e.g., 4.73 vs. 8.03 pixels).

CAMA 是一个无需 LiDAR 输入的视觉为中心的方法，能够生成高质量的静态地图元素的三维标注，并且在所有周围相机上具有高度的重投影精度并且在整个序列中具有时空连续性。

基于视觉的静态地图元素标注方法

A Vision-Centric Approach for Static Map Element Annotation

In this paper, we improve the single-vehicle 3D object detection models using
LiDAR by extending their capacity to process point cloud sequences instead of
individual point clouds. In this step, we extend our previous work on
rectification of the shadow effect in the concatenation of point clouds to
boost the detection accuracy of multi-frame detection models. Our extension
includes incorporating HD Map and distilling an Oracle model. Next, we further
increase the performance of single-vehicle perception using multi-agent
collaboration via Vehicle-to-everything (V2X) communication. We devise a simple
yet effective collaboration method that achieves better bandwidth-performance
tradeoffs than prior arts while minimizing changes made to single-vehicle
detection models and assumptions on inter-agent synchronization. Experiments on
the V2X-Sim dataset show that our collaboration method achieves 98% performance
of the early collaboration while consuming the equivalent amount of bandwidth
usage of late collaboration which is 0.03% of early collaboration. The code
will be released at this https URL

本文介绍一种新的通过点云序列检测车辆 3D 物体的方法，采用 LiDAR 传感器解决阴影效应与 HD 地图配合，并使用 V2X 通信技术提高车辆感知性能，同时优化带宽性能.

实用协作感知：异步和多智能体三维物体检测框架

Practical Collaborative Perception: A Framework for Asynchronous and  Multi-Agent 3D Object Detection

High-definition (HD) map serves as the essential infrastructure of autonomous
driving. In this work, we build up a systematic vectorized map annotation
framework (termed VMA) for efficiently generating HD map of large-scale driving
scene. We design a divide-and-conquer annotation scheme to solve the spatial
extensibility problem of HD map generation, and abstract map elements with a
variety of geometric patterns as unified point sequence representation, which
can be extended to most map elements in the driving scene. VMA is highly
efficient and extensible, requiring negligible human effort, and flexible in
terms of spatial scale and element type. We quantitatively and qualitatively
validate the annotation performance on real-world urban and highway scenes, as
well as NYC Planimetric Database. VMA can significantly improve map generation
efficiency and require little human effort. On average VMA takes 160min for
annotating a scene with a range of hundreds of meters, and reduces 52.3% of the
human cost, showing great application value.

本文提出了一种系统化的向量地图注释框架（VMA），用于有效生成大规模自动驾驶场景的高清地图。VMA 采用分治注释方案解决了高清地图生成中的空间可扩展性问题，并将地图要素抽象为不同的几何图案统一点序列表示，从而提高了生成地图的效率和灵活性。实测结果表明，VMA 显著提高了地图生成效率，同时降低了人力成本。

VMA: 大规模自驾场景分治向量化地图注释系统

VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale  Driving Scene

High-definition (HD) map provides abundant and precise environmental
information of the driving scene, serving as a fundamental and indispensable
component for planning in autonomous driving system. We present MapTR, a
structured end-to-end Transformer for efficient online vectorized HD map
construction. We propose a unified permutation-equivalent modeling approach,
i.e., modeling map element as a point set with a group of equivalent
permutations, which accurately describes the shape of map element and
stabilizes the learning process. We design a hierarchical query embedding
scheme to flexibly encode structured map information and perform hierarchical
bipartite matching for map element learning. MapTR achieves the best
performance and efficiency with only camera input among existing vectorized map
construction approaches on nuScenes dataset. In particular, MapTR-nano runs at
real-time inference speed ($25.1$ FPS) on RTX 3090, $8\times$ faster than the
existing state-of-the-art camera-based method while achieving $5.0$ higher mAP.
Even compared with the existing state-of-the-art multi-modality method,
MapTR-nano achieves $0.7$ higher mAP, and MapTR-tiny achieves $13.5$ higher mAP
and $3\times$ faster inference speed. Abundant qualitative results show that
MapTR maintains stable and robust map construction quality in complex and
various driving scenes. MapTR is of great application value in autonomous
driving. Code and more demos are available at
https://github.com/hustvl/MapTR.

MapTR 是一个高效的在线矢量高清地图构建系统，采用基于点集的建模方法和分层查询嵌入方案，可用于自动驾驶系统中进行规划，具有比现有方法更好的性能和效率。