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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="An approach to generate minimal semantic 2D maps from street level images">
<meta name="keywords" content="MapMaker, Semantic Maps, Flatladia, Transformers">
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<title>Maps from Motion (MfM)</title>
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<a class="navbar-item" href="https://3dvconf.github.io/2025/">
3DV 2025
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<section class="hero">
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<h1 class="title is-1 publication-title">Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse
Multi-view Images</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://www.iit.it/people-details/-/people/matteo-toso">Matteo Toso</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://www.iit.it/people-details/-/people/stefano-fiorini">Stefano Fiorini</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://stuart-james.com/">Stuart James</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://www.iit.it/it/people-details/-/people/alessio-delbue">Alessio Del Bue </a><sup>1</sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Istituto Italiano di Tecnologia (IIT)</span>
<span class="author-block"><sup>2</sup>Durham University</span>
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<span>Data</span>
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alt="Maps from Motion">
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<h2 class="subtitle has-text-centered">
<em> The Maps from Motion (MfM) Concept.</em> From a set of uncalibrated images (a) we extract 2D detections of static urban objects (b), and generate local top-down 2D maps representing the spatial arrangement of the objects with respect to each image (c). We then learn how to register all local maps in the same reference frame (d), to generate a common global map with all objects present in the scene (e).
</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
World-wide detailed 2D maps require enormous collective efforts. <em> OpenStreetMap </em> is
the result of 11 million registered users manually annotating the GPS location of over 1.75
billion entries, including distinctive landmarks and common urban objects. At the same time,
manual annotations can include errors and are slow to update, limiting the map's accuracy.
<em> Maps from Motion </em> (MfM) is a step forward to automatize such time-consuming map
making procedure by computing 2D maps of semantic objects directly from a collection of
uncalibrated multi-view images.
</p>
<p>
From each image, we extract a set of object detections, and estimate their spatial
arrangement in a top-down local map centered in the reference frame of the camera that
captured the image.
Aligning these local maps is not a trivial problem, since they provide incomplete, noisy
fragments of the scene, and matching detections across them is unreliable because of the
presence of repeated pattern and the limited appearance variability of urban objects.
We address this with a novel graph-based framework, that encodes the spatial and semantic
distribution of the objects detected in each image, and learns how to combine them to
predict the objects' poses in a global reference system, while taking into account all
possible detection matches and preserving the topology observed in each image.
Despite the complexity of the problem, our best model achieves global 2D registration with
an average accuracy within 4 meters (<em> i.e., </em> below GPS accuracy) even on sparse sequences with
strong viewpoint change, on which COLMAP has an 80% failure rate.
We provide extensive evaluation on synthetic and real-world data, showing how the method
obtains a solution even in scenarios where standard optimization techniques fail.
</p>
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<h2 class="title is-3">Acknowledgement</h2>
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<p>
This work is supported by PNRR MUR Project Cod. PE0000013 <em>
"Future Artificial Intelligence Research (FAIR)"
</em> - CUP J53C22003010006, and by the European Union's
Horizon research and innovation programme
<em>"DCitizens" </em> (grant agreement No 101079116) and
<em>"Bauhaus of the Seas Sails" </em> (grant agreement No 101079995).
</p>
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<h2 class="title">BibTeX</h2>
<pre><code>@article{park2021nerfies,
author = {Matteo, Toso and Stefano, Fiorini and Stuart, James and Alessio, Del Bue},
title = {Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images},
journal = {3DV},
year = {2025},
}</code></pre>
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