VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale
Driving Scene
- URL: http://arxiv.org/abs/2304.09807v2
- Date: Sun, 27 Aug 2023 13:58:18 GMT
- Title: VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale
Driving Scene
- Authors: Shaoyu Chen, Yunchi Zhang, Bencheng Liao, Jiafeng Xie, Tianheng Cheng,
Wei Sui, Qian Zhang, Chang Huang, Wenyu Liu, Xinggang Wang
- Abstract summary: We build up a systematic vectorized map annotation framework (termed VMA) for efficiently generating HD map of large-scale driving scene.
VMA is highly efficient and requires negligible human effort, and flexible in terms of spatial scale and element type.
On average VMA takes 160min for annotating a scene with a range of hundreds of meters, and reduces 52.3% of the human cost.
- Score: 41.110429729268496
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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. Code:
https://github.com/hustvl/VMA.
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