Dan Barnes

Research Engineer @ Facebook Reality Labs

[Edward Miller @TweetEdMiller]

📢 Today, our team is excited to announce 𝑺𝒄𝒆𝒏𝒆𝑺𝒄𝒓𝒊𝒑𝒕: an end-to-end method for scene reconstruction from @RealityLabs Research, which enables AI agents to understand and describe 3D spaces.

https://t.co/Te0pNtkFfS

/🧵

[Simon Green @simesgreen]

This is from my colleagues in London, and proves that LLMs can do more than talk nonsense! :)

[Edward Miller @TweetEdMiller]

Join our Tutorial at #CVPR2023 to learn more about Meta's Project Aria, and how university labs can work with the device for independent research.

Learn more at https://t.co/SPg1UyGQPI

[Dmytro Mishkin @ducha_aiki]

OrienterNet: Visual Localization in 2D Public Maps with Neural Matching

@pesarlin et 10 al.

tl;dr: Photo -> neural bird's view -> matching vs encoded 2D map -> profit!

https://t.co/eBPnY1MajK

[Ego4D @ego4_d]

@_rohitgirdhar_ @anfurnari @Sid__Bansal @MikeShou1 @SatwikKottur @cfeichtenhofer @joaocarreira @CMU_Robotics @RehgJim @3scorciav @akadada1213 @SiweiZhang13 @mingfeiy and the team from @RealityLabs will briefing us on the latest with #ProjectAria and their mission to enable academia access to these unqiue devices, which include stereo cameras, dual inertial measurement units, spatialized microphones, eye tracking cameras, and more!

[Tony Ng @tonyngjichun]

We're presenting our #CVPR2022 paper "NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning" during poster session 3.1 tomorrow Jun 23 at 10am. Come check it out - we'll be at stand 211! A short thread 1/N

[Daniel DeTone @ddetone]

Made it to #CVPR22 in New Orleans! Come check out my talk about our NeRF inspired -- hybrid sparse / dense 3D representation called Nerfels in the Image Matching Workshop at 3:10-3:20pm local time. Paper link here for more info: https://t.co/YKTnPbpqPO

[Edward Miller @TweetEdMiller]

Ahead of #CVPR2022 , I’m excited to share the open dataset of Project Aria data from Meta Reality Labs, along with accompanying open research tools designed to accelerate AI and ML research. https://t.co/tPuJFLMjG1
A little about the dataset and why I think it’s so exciting…
1/n

[Dan Barnes @danbarnes333]

@pshaw11 @HarryTincknell My skills end at turning it off and on...

[Oxford Robotics Institute @oxfordrobots]

The 12th paper in our countdown to #icra2020 is 'RSL-Net: Localising in Satellite Images from a Radar on the Ground' by Tim Y. Tang, Daniele De Martini, @danbarnes333, and @paul_m_newman

Video: https://t.co/1iWYDCTum4
@MRG_Oxford

[Oxford Robotics Institute @oxfordrobots]

The tenth paper in our countdown to #icra2020 is 'Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning' by @StefanSaftescu, @mtt_gdd, D. De Martini, @danbarnes333 and @paul_m_newman

Video: https://t.co/S2d737dZvH
@MRG_Oxford

[Oxford Robotics Institute @oxfordrobots]

The seventh paper in our countdown to #icra2020 is 'Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar' by @danbarnes333 and @IngmarPosner
Video: https://t.co/eEdEJRrFXN
Blog: https://t.co/09AmQEnh49

[Oxford Robotics Institute @oxfordrobots]

In our countdown to #ICRA2020, we are sharing one of our accepted papers each day, starting with 'The Oxford Radar RobotCar Dataset'
by @danbarnes333, @mtt_gdd, @PMurcutt, @paul_m_newman, @IngmarPosner

Video: https://t.co/YjL7b4skOv
More Info: https://t.co/weGuLlvZ3e

[Dan Barnes @danbarnes333]

Interested in keypoint learning in radar? Excited to share some recent work with @IngmarPosner on keypoint learning for odometry estimation and metric localisation using a @Navtechradar!
https://t.co/712O1zdNOa
@a2i_oxford @oxfordrobots #Robotics #SelfDrivingCars

[Mobile Robotics Group @MRG_Oxford]

the pre-print for our paper on learning a metric space for robust topological localisation with FMCW radar is available! https://t.co/QVn82gykNE
@StefanSaftescu @mtt_gdd @danbarnes333 @paul_m_newman

[Dan Barnes @danbarnes333]

Proud to share Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information with Rob Weston and @IngmarPosner. A new state of the art in Radar Odometry on the Oxford Radar RobotCar Dataset!
https://t.co/WZSWHVjVTg
@a2i_oxford @oxfordrobots #Robotics

[Ingmar Posner @IngmarPosner]

If you care about artefact-free radar embeddings and state-of-the-art radar odometry take a look at recent work by @danbarnes333 and Rob Weston. @a2i_oxford @oxfordrobots #Robotics #AutonomousVehicles #SelfDrivingCars
https://t.co/8jtyOEkuP7

[Dan Barnes @danbarnes333]

@jimdsouza @oxfordrobots @mtt_gdd @PMurcutt @paul_m_newman @IngmarPosner Thanks for your interest in the dataset! Registration is open to any academic institution (.edu / .ac or any address we can verify). If you are not at an academic institution but are interested in using the dataset please get in touch! Thanks

[Oxford Robotics Institute @oxfordrobots]

ORI are excited to release the Oxford Radar RobotCar Dataset!
The dataset contains 280km of radar data, on top of a full sensor suite, to accelerate research in this domain.
Visit https://t.co/Hk9DpqgKvm to read more!
@danbarnes333 @mtt_gdd @PMurcutt @paul_m_newman @IngmarPosner