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<!DOCTYPE html>
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content="MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping.">
<meta name="keywords" content="Vector HD mapping, Autonomous driving">
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<title>MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping</title>
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<h1 class="title is-1 publication-title">MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping</h1>
<div class="is-size-4 publication-authors">
<span class="author-block">
ECCV 2024 (Oral)
</div>
<div class="is-size-4 publication-authors">
<span class="author-block">
<a href="https://jcchen.me/">Jiacheng Chen</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="https://ivenwu.com/">Yuefan Wu</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="https://christinatan0704.github.io/mysite/">Jiaqi Tan</a><sup>1*</sup>,
</span>
<span class="author-block">
<a href="https://www.cs.sfu.ca/~hangma/">Hang Ma</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://yasu-furukawa.github.io/">Yasutaka Furukawa</a><sup>1,2</sup>
</span>
</div>
<div class="is-size-5 publication-affiliations">
<span class="author-block"> <sup>1</sup> <a href="https://www.sfu.ca/"> Simon Fraser University</a> </span>
   
<span class="author-block"> <sup>2</sup> <a href="https://wayve.ai/">Wayve</a> </span>
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<span>arXiv</span>
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<span >Code </span>
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<h2 class="title is-2">Abstract</h2>
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<p>
This paper presents a vector HD-mapping algorithm that formulates the mapping as a tracking
task and uses a history of memory latents to ensure consistent reconstructions over time.
</p>
<p>
Our method, <i>MapTracker</i>, accumulates a sensor stream into memory buffers of two latent
representations: 1) Raster latents in the bird's-eye-view (BEV) space and 2) Vector latents over
the road elements (i.e., pedestrian-crossings, lane-dividers, and road-boundaries).
The approach borrows the query propagation paradigm from the tracking literature that explicitly associates tracked road elements from the previous frame to the current, while fusing a subset of memory latents selected with distance strides to further enhance temporal consistency.A vector latent is decoded to reconstruct the geometry of a road element.
</p>
<p>
The paper further makes benchmark contributions by 1) Improving processing code for existing datasets
to produce consistent ground truth with temporal alignments and 2) Augmenting existing mAP metrics with consistency checks. MapTracker significantly outperforms existing methods on both nuScenes and Agroverse2 datasets by over 8% and 19% on the conventional and the new consistency-aware metrics, respectively.
</p>
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<!-- Comparisons -->
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<h2 class="title is-4">Comparisons with baselines</h2>
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<h2 class="title is-4">More merged global reconstruction results</h2>
<h3 class="title is-5" style="text-align: center;">nuScenes dataset</h3>
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<br/>
<br/>
<h3 class="title is-5" style="text-align: center;">Agroverse2 dataset</h3>
<video poster="" autoplay="" muted="" loop="" style="pointer-events: none;">
<source src="./static/videos/random_av2.mp4" type="video/mp4">
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</section>
<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@article{chen2024maptracker,
author = {Chen, Jiacheng and Wu, Yuefan and Tan, Jiaqi and Ma, Hang and Furukawa, Yasutaka},
title = {MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping},
journal = {arXiv preprint arXiv:2403.15951},
year = {2024},
}</code></pre>
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