Summary of Unveiling the Hidden: Online Vectorized Hd Map Construction with Clip-level Token Interaction and Propagation, by Nayeon Kim et al.
Unveiling the Hidden: Online Vectorized HD Map Construction with Clip-Level Token Interaction and Propagation
by Nayeon Kim, Hongje Seong, Daehyun Ji, Sujin Jang
First submitted to arxiv on: 17 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As a machine learning educator, I summarize the abstract as follows: A crucial task in autonomous driving is predicting and constructing road geometric information. Recent studies have shown improved performance in high-definition (HD) map construction, but there has been insufficient investigation of temporal information across adjacent input frames. The authors introduce MapUnveiler, a novel paradigm for clip-level vectorized HD map construction that explicitly unveils occluded map elements within a clip and associates inter-clip information through token propagation. The proposed pipeline runs efficiently by avoiding redundant computation with temporal stride while building global map relationships. Experiments demonstrate state-of-the-art performance on nuScenes and Argoverse2 benchmark datasets, and MapUnveiler outperforms state-of-the-art approaches in heavily occluded driving road scenes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For curious learners or general audiences, I simplify the abstract as follows: This paper is about making maps for self-driving cars. Maps are important because they help the car know what’s on the road. The authors developed a new way to make maps that works really well. They called it MapUnveiler. It helps find hidden map information and uses it to make better maps. The results show that this method is much better than other methods in making accurate maps. |
Keywords
» Artificial intelligence » Machine learning » Token
