SparseMeXT Unlocking the Potential of Sparse Representations for HD Map Construction
Anqing Jiang, Jinhao Chai, Yu Gao, Yiru Wang, Yuwen Heng, Zhigang Sun, Hao Sun, Zezhong Zhao, Li Sun, Jian Zhou, Lijuan Zhu, Shugong Xu, Hao Zhao

TL;DR
This paper enhances sparse representation techniques for HD map construction, achieving state-of-the-art accuracy and efficiency, and demonstrating that sparse methods can surpass dense approaches with proper architectural and algorithmic improvements.
Contribution
The authors introduce a specialized network architecture, a sparse-dense segmentation auxiliary task, and a denoising module guided by physical priors, significantly improving sparse HD map construction.
Findings
SparseMeXt-Tiny achieves 55.5% mAP at 32 fps.
SparseMeXt-Base attains 65.2% mAP.
SparseMeXt-Large reaches 68.9% mAP at over 20 fps.
Abstract
Recent advancements in high-definition \emph{HD} map construction have demonstrated the effectiveness of dense representations, which heavily rely on computationally intensive bird's-eye view \emph{BEV} features. While sparse representations offer a more efficient alternative by avoiding dense BEV processing, existing methods often lag behind due to the lack of tailored designs. These limitations have hindered the competitiveness of sparse representations in online HD map construction. In this work, we systematically revisit and enhance sparse representation techniques, identifying key architectural and algorithmic improvements that bridge the gap with--and ultimately surpass--dense approaches. We introduce a dedicated network architecture optimized for sparse map feature extraction, a sparse-dense segmentation auxiliary task to better leverage geometric and semantic cues, and a…
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Taxonomy
TopicsVideo Analysis and Summarization · Multimedia Communication and Technology · Image Retrieval and Classification Techniques
