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99        <a class="navbar-link">
100          More Research
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103          <a class="navbar-item" href="/crocodl/">
104            CroCoDL
105          </a>
106          <a class="navbar-item" href="https://lamar.ethz.ch/">
107            LaMAR
108          </a>
109          <a class="navbar-item" href="/workshop/">
110            ICCV 2025 Workshop - CroCoDL
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117</nav>
118
119<center><img src="../static/images/CroCo1.png" width="1000"></center>
120<section class="hero">
121  <div class="hero-body">
122    <div class="container is-max-desktop">
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124        <div class="column has-text-centered">
125          <h1 class="title is-1 publication-title">Cross-device Collaborative Dataset for Localization</h1>
126          <div class="is-size-5 publication-authors">
127            <span class="author-block">
128              <a href="https://hermannblum.net/">Hermann Blum</a><sup>1,2</sup>,</span>
129            <span class="author-block">
130              <a href="https://www.linkedin.com/in/alemercurio/?trk=public_post_comment_actor-image&originalSubdomain=ch">Alessandro Mercurio</a><sup>2</sup>,</span>
131            <span class="author-block">
132              <a href="https://joshuaoreilly.com/">Joshua O'Reilly</a><sup>2</sup>,
133            </span>
134            <span class="author-block">
135              <a href="https://www.linkedin.com/in/timengelbracht/?locale=de_DE">Tim Engelbracht</a><sup>2</sup>,
136            </span>
137            <span class="author-block">
138              <a href="https://dusmanu.com/">Mihai Dusmanu</a><sup>3</sup>,
139            </span>
140            <span class="author-block">
141              <a href="https://people.inf.ethz.ch/marc.pollefeys/">Marc Pollefeys</a><sup>2,3</sup>,
142            </span>
143            <span class="author-block">
144              <a href="https://zuriabauer.com/">Zuria Bauer</a><sup>2</sup>
145            </span>
146          </div>
147
148          <div class="is-size-5 publication-authors">
149            <span class="author-block"><sup>1</sup>Lamarr Institute/Uni Bonn</span>
150            <span class="author-block"><sup>2</sup>ETH Zurich</span>
151            <span class="author-block"><sup>3</sup>Microsoft</span>
152          </div>
153
154          <div class="column has-text-centered">
155            <div class="publication-links">
156              <!-- PDF Link. -->
157              <span class="link-block">
158                <a href="https://openaccess.thecvf.com/content/CVPR2025/html/Blum_CroCoDL_Cross-device_Collaborative_Dataset_for_Localization_CVPR_2025_paper.html"
159                   class="external-link button is-normal is-rounded is-dark">
160                  <span class="icon">
161                      <i class="fas fa-file-pdf"></i>
162                  </span>
163                  <span>Paper</span>
164                </a>
165              </span>
166              <span class="link-block">
167                <a href=""
168                   class="external-link button is-normal is-rounded is-dark">
169                  <span class="icon">
170                      <i class="ai ai-arxiv"></i>
171                  </span>
172                  <span>arXiv - Soon</span>
173                </a>
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187                <a href="https://github.com/cvg/crocodl-benchmark"
188                   class="external-link button is-normal is-rounded is-dark">
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190                      <i class="fab fa-github"></i>
191                  </span>
192                  <span>Code</span>
193                  </a>
194              </span>
195              <!-- Dataset Link. -->
196              <span class="link-block">
197                <a href="https://huggingface.co/CroCoDL"
198                   class="external-link button is-normal is-rounded is-dark">
199                  <span class="icon">
200                      <i class="far fa-images"></i>
201                  </span>
202                  <span>Data</span>
203                  </a>
204              </span>
205              <span class="link-block">
206                <a href="https://www.codabench.org/competitions/9471/"
207                   class="external-link button is-normal is-rounded is-dark">
208                  <span class="icon">
209                      <i class="fas fa-trophy"></i>
210                  </span>
211                  <span>Challenge</span>
212                  </a>
213              </span>
214            </div>
215
216          </div>
217        </div>
218      </div>
219    </div>
220  </div>
221</section>
222
223<section class="hero teaser">
224  <div class="container is-max-desktop">
225    <div class="hero-body">
226      <img src="../static/images/CrocoTeaser.png" width="2000" height="1000">
227      <h2 class="subtitle has-text-centered">
228        <span class="dnerf">CroCoDL:</span> the first dataset to contain sensor
229        recordings from real-world robots, phones, and mixed-reality
230        headsets, covering a total of 10 challenging locations to
231        benchmark cross-device and human-robot visual registra-
232        tion.
233      </h2>
234    </div>
235  </div>
236</section>
237
238
239<section class="section">
240  <div class="container is-max-desktop">
241    <!-- Abstract. -->
242    <div class="columns is-centered has-text-centered">
243      <div class="column is-four-fifths">
244        <h2 class="title is-3"><span class="dnerf">Abstract</span></h2>
245        <div class="content has-text-justified">
246          <p>
247            Accurate localization plays a pivotal role in the autonomy of systems operating in unfamiliar environments, particularly when interaction with humans is expected. High-accuracy visual localization systems encompass various components, such as feature extractors, matchers, and pose estimation methods. This complexity translates to the necessity of robust evaluation settings and pipelines. However, existing datasets and benchmarks primarily focus on single-agent scenarios, overlooking the critical issue of cross-device localization. Different agents with different sensors will show their own specific strengths and weaknesses, and the data they have available varies substantially. 
248          </p>
249          <p>
250            This work addresses this gap by enhancing an existing augmented reality visual localization benchmark with data from legged robots, and evaluating human-robot, cross-device mapping and localization. Our contributions extend beyond device diversity and include high environment variability, spanning ten distinct locations ranging from disaster sites to art exhibitions. Each scene in our dataset features recordings from robot agents, hand-held and head-mounted devices, and high-accuracy ground truth LiDAR scanners, resulting in a comprehensive multi-agent dataset and benchmark. This work represents a significant advancement in the field of visual localization benchmarking, with key insights into the performance of cross-device localization methods across diverse settings.
251          </p>
252        </div>
253      </div>
254    </div>
255    <!--/ Abstract. -->
256  </div>
257</section>
258
259
260<section class="hero is-light is-small">
261  <div class="hero-body">
262    <div class="container">
263      <h2 class="title is-3"><center><span class="dnerf">Renderings vs. real CroCoDL data</span></center></h2>
264      <div id="results-carousel" class="carousel results-carousel">
265        <div class="item item-steve">
266          <img src="../static/images/armadillo/dataset1.png" alt="Steve", height="475">
267        </div>
268        <div class="item item-steve">
269          <img src="../static/images/armadillo/dataset2.png" alt="Steve", height="475">
270        </div>
271        <div class="item item-steve">
272          <img src="../static/images/armadillo/dataset3.jpg" alt="Steve", height="475">
273        </div>
274        <div class="item item-steve">
275          <img src="../static/images/armadillo/dataset6.jpg" alt="Full Body">
276        </div>
277        <div class="item item-steve">
278          <img src="../static/images/armadillo/dataset8.jpg" alt="Blue Shirt">
279        </div>
280        <div class="item item-steve">
281          <img src="../static/images/armadillo/dataset10.png" alt="Mask">
282        </div>
283        <div class="item item-steve">
284          <img src="../static/images/armadillo/dataset13.png" alt="Coffee">
285        </div>
286        <div class="item item-steve">
287          <img src="../static/images/armadillo/dataset17.png" alt="Toby">
288        </div>
289        <div class="item item-steve">
290          <img src="../static/images/armadillo/dataset19.png" alt="Toby">
291        </div>
292        <div class="item item-steve">
293          <img src="../static/images/armadillo/dataset20.png" alt="Toby">
294        </div>
295        <div class="item item-steve">
296          <img src="../static/images/armadillo/dataset22.png" alt="Toby">
297        </div>
298        <div class="item item-steve">
299          <img src="../static/images/armadillo/dataset27.png" alt="Toby">
300        </div>
301      </div>
302      <br>
303      <h2 class="subtitle has-text-centered">
304        <span class="dnerf">Qualitative Results.</span> Good alignment between rendering and real
305        image validates correct camera pose with respect to the NavVis-based ground truth scan from which we render here.
306      </h2>
307    </div>
308  </div>
309</section>
310
311<section class="hero teaser">
312  <div class="container is-max-desktop">
313    <div class="hero-body">
314      <br>
315      <h2 class="title is-3"><center><span class="dnerf">Locations</span></center></h2>
316      <img src="../static/images/Locations.png" width="2500" height="1000">
317      <center>
318      <img src="../static/images/Locations2.png" width="800" height="1000"></center>
319      <h2 class="subtitle has-text-centered">
320        <span class="dnerf">New locations of the CroCoDL dataset.</span> Each location has high-quality meshes, obtained from LiDAR, which
321        are registered with numerous phone, AR headset, and robotic sequences.
322      </h2>
323    </div>
324  </div>
325</section>
326
327<section class="hero is-light is-small">
328  <div class="hero-body">
329    <div class="container">
330      <h2 class="title is-3"><span class="dnerf"><center>Commonly used datasets for visual localization and SLAM</center></span></h2>
331<table border="0" style="border-collapse: collapse; text-align:center; width: 100%;">
332  <br>
333  <thead>
334      <tr>
335          <th>Dataset</th>
336          <th>Motion</th>
337          <th>Env.</th>
338          <th>Locations</th>
339          <th>Changes</th>
340          <th>Sensors</th>
341          <th>GT pose accuracy</th>
342          <th>Seqs.</th>
343      </tr>
344  </thead>
345  <tbody>
346      <tr>
347          <td>KITTI</td>
348          <td>🚗</td>
349          <td>⬛</td>
350          <td>1</td>
351          <td>🏃🌦️</td>
352          <td>RGB, LiDAR, IMU</td>
353          <td>&lt;10cm (RTKGPS)</td>
354          <td>22</td>
355      </tr>
356      <tr>
357          <td>TUM RGBD</td>
358          <td>✋🛼</td>
359          <td>⬜</td>
360          <td>2</td>
361          <td>🏃</td>
362          <td>RGB-D, IMU</td>
363          <td>1mm (mocap)</td>
364          <td>80</td>
365      </tr>
366      <tr>
367          <td>Malaga</td>
368          <td>🚗</td>
369          <td>⬛</td>
370          <td>1</td>
371          <td>🏃🌦️</td>
372          <td>RGB, IMU</td>
373          <td>(GPS)</td>
374          <td>15</td>
375      </tr>
376      <tr>
377          <td>EUROC</td>
378          <td>🛸</td>
379          <td>⬜</td>
380          <td>2</td>
381          <td>-</td>
382          <td>RGB, IMU</td>
383          <td>1mm (mocap)</td>
384          <td>11</td>
385      </tr>
386       <tr>
387        <td>NCLT</td>
388        <td>🛼</td>
389        <td>⬜⬛</td>
390        <td>1</td>
391        <td>🏃🪑🌒🌦️</td>
392        <td>RGB, LiDAR, IMU, GNSS</td>
393        <td>&lt;10cm (GPS + IMU + LiDAR)</td>
394        <td>27</td>
395      </tr>
396      <tr>
397        <td>PennCOSYVIO</td>
398        <td>✋</td>
399        <td>⬜⬛</td>
400        <td>1</td>
401        <td>🏃🌦️</td>
402        <td>RGB, IMU</td>
403        <td>15cm (visual tags)</td>
404        <td>4</td>
405      </tr>
406      <tr>
407        <td>TUM VIO</td>
408        <td>✋</td>
409        <td>⬜⬛</td>
410        <td>4</td>
411        <td>-</td>
412        <td>RGB, IMU</td>
413        <td>1mm (mocap ends)</td>
414        <td>28</td>
415      </tr>
416      <tr>
417        <td>
417UZH-FPV</td>
418        <td>🛸</td>
419        <td>⬜⬛</td>
420        <td>2</td>
421        <td>-</td>
422        <td>RGB, event camera, IMU</td>
423        <td>~1cm (total station + VI-BA)</td>
424        <td>28</td>
425      </tr>
426      <tr>
427        <td>ETH3D SLAM</td>
428        <td>✋</td>
429        <td>⬜</td>
430        <td>1</td>
431        <td>-</td>
432        <td>RGB, depth, IMU</td>
433        <td>1mm (mocap)</td>
434        <td>96</td>
435      </tr>
436      <tr>
437        <td>Newer College</td>
438        <td>✋</td>
439        <td>⬜⬛</td>
440        <td>1</td>
441        <td>-</td>
442        <td>RGB, LiDAR, IMU</td>
443        <td>3cm (LiDAR ICP)</td>
444        <td>3</td>
445      </tr>
446      <tr>
447        <td>OpenLoris-Scene</td>
448        <td>🛼</td>
449        <td>⬜</td>
450        <td>5</td>
451        <td>🏃🪑</td>
452        <td>RGB-D, IMU, wheel odom.</td>
453        <td>&lt;10cm (2D LiDAR)</td>
454        <td>22</td>
455      </tr>
456      <tr>
457        <td>TartanAir</td>
458        <td>syn.</td>
459        <td>⬜⬛</td>
460        <td>30</td>
461        <td>-</td>
462        <td>RGB</td>
463        <td>perfect (synthetic)</td>
464        <td>30</td>
465      </tr>
466      <tr>
467        <td>UMA VI</td>
468        <td>✋🚗</td>
469        <td>⬜⬛</td>
470        <td>2</td>
471        <td>-</td>
472        <td>RGB, IMU</td>
473        <td>(visual tags)</td>
474        <td>32</td>
475      </tr>
476      <tr>
477        <td>UrbanLoco</td>
478        <td>🚗</td>
479        <td>⬛</td>
480        <td>12</td>
481        <td>🏃🪑</td>
482        <td>RGB, LiDAR, IMU, GNSS, SPAN-CPT</td>
483        <td>12</td>
484      </tr>
485      <tr>
486        <td>Naver Labs</td>
487        <td>🛼</td>
488        <td>⬜</td>
489        <td>5</td>
490        <td>🏃 🪑</td>
491        <td>RGB, LiDAR, IMU</td>
492        <td>&lt;10cm (LiDAR SLAM <br>and SfM)</td>
493        <td>10</td>
494    </tr>
495      <tr>
496        <td>HILTI SLAM</td>
497        <td>✋</td>
498        <td>⬜⬛</td>
499        <td>8</td>
500        <td>-</td>
501        <td>RGB, LiDAR, IMU</td>
502        <td>&lt;5mm (total station)</td>
503        <td>12</td>
504    </tr>
505    <tr>
506        <td>Graco</td>
507        <td>🛼🛸</td>
508        <td>⬛</td>
509        <td>1</td>
510        <td>🏃 🌦️</td>
511        <td>RGB, LiDAR, GPS, IMU</td>
512        <td>≈1cm (GNSS)</td>
513        <td>14</td>
514    </tr>
515    <tr>
516        <td>FusionPortable</td>
517        <td>2	&isin; ✋🦿🛼🚙</td>
518        <td>⬜⬛</td>
519        <td>9</td>
520        <td>-</td>
521        <td>RGB, event cameras, LiDAR, IMU, GPS</td>
522        <td>≈1cm (GNSS RTK)</td>
523        <td>41</td>
524    </tr>
525      <tr class="line-between">
526        <td>LaMAR</td>
527        <td>✋🥽</td>
528        <td>⬜⬛</td>
529        <td>3</td>
530        <td>🏃🪑🌒🏗️🌦️</td>
531        <td>RGB, LiDAR, depth, IMU, WiFi/BT</td>
532        <td>&lt;10cm (LiDAR + PGO + PGO-BA)</td>
533        <td>500</td>
534      </tr>
535      <tr>
536        <td><b>CroCoDL</b></td>
537        <td>✋🥽🦿[🛸]</td>
538        <td>⬜⬛</td>
539        <td>10</td>
540        <td>🏃🪑🌒🏗️🌦️</td>
541        <td>RGB, LiDAR, depth, IMU, WiFi/BT</td>
542        <td>~10cm (LiDAR + PGO + PGO-BA)</td>
543        <td>500 <strong>+800</strong></td>
544    </tr>
545  </tbody>
546    </table>
547    <br>
548    <center><h3 class="title is-3"><span class="dnerf"><p><strong>Legend</strong> </p></h3></span>
549      <p><strong><span class="dnerf">Environment:</span></strong><span style="background-color: #f0f0f0; padding: 2px 5px; border-radius: 4px;">⬜ inside</span>, 
550      <span style="background-color: #f0f0f0; padding: 2px 5px; border-radius: 4px;">⬛ outside</span>;</p>
551      
552      <p><strong><span class="dnerf">Changes:</span></strong> 🏃 Structural changes due to moving people, 🪑 long-term changes due to displaced furniture, 🌦️ weather, 🌒 day-night, 🏗️ construction work;</p>
553      
554      <p><strong><span class="dnerf">Trajectory motion from sensors mounted on:</span></strong> 🛼 ground vehicle, 🦿 legged robot, 🛸 drone, 🚙 car, ✋ hand-held, 🥽 head-mounted, <em>‘syn.’ synthetic.</em></p>
555      
556      <p>(noted with *: at most 2 devices are recorded in the same location; [🛸]: not aligned, due to safety / permission reasons - we could only capture drone footage in 8/10 locations)</p>
557    </center>
558    </div>
559  </div>
560</section>
561<br>
562<section class="section" id="BibTeX">
563  <div class="container is-max-desktop content">
564    <h2 class="title">BibTeX</h2>
565    <pre><code>@inproceedings{blum2025crocodl,
566  author    = {Blum, Hermann and Mercurio, Alessandro and O'Reilly, Josua and Engelbracht, Tim and Dusmanu, Mihai and Pollefeys, Marc and Bauer, Zuria},
567  title     = {CroCoDL: Cross-device Collaborative Dataset for Localization},
568  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
569  year      = {2025},
570}</code></pre>
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