Bart Duisterhof
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bardienus.bsky.social
Bart Duisterhof
@bardienus.bsky.social
PhD Student @cmurobotics.bsky.social with @jeff-ichnowski.bsky.social || DUSt3R Research Intern @naverlabseurope || 4D Vision for Robot Manipulation 📷

He/Him - https://bart-ai.com
Reposted by Bart Duisterhof
For which the code is also available github.com/naver/pow3r
GitHub - naver/pow3r
Contribute to naver/pow3r development by creating an account on GitHub.
github.com
June 12, 2025 at 1:41 PM
Reposted by Bart Duisterhof
🔗 Project Website: rayst3r.github.io
📄 arXiv: arxiv.org/abs/2506.05285
🚀 Code: github.com/Duisterhof/...
🤗 HF Demo: Coming (very) soon!

@CMU_Robotics @SCSatCMU @nvidia @NVIDIAAI @NVIDIARobotics
GitHub - Duisterhof/rayst3r
Contribute to Duisterhof/rayst3r development by creating an account on GitHub.
github.com
June 6, 2025 at 1:52 PM
Big thanks to the awesome contributors to this project!👏 Jan Oberst, @bowenwen_me, @BirchfieldStan, @RamananDeva and @jeff_ichnowski. Also thanks to OctMAE author @s1wase, @nvidia for sponsoring compute 🖥️, and the scientists at @naverlabseurope for the inspiration! 🧗‍♂️
June 6, 2025 at 1:52 PM
We also study the impact of the confidence threshold on reconstruction quality. Our ablations suggest setting a higher confidence threshold improves accuracy, while limiting completeness and edge-bleeding. Users can tune the threshold for application-specific requirements 🎛️.
June 6, 2025 at 1:52 PM
We evaluate RaySt3R against the baselines on synthetic and real-world datasets. The results suggest RaySt3R achieves zero-shot generalization to the real world, and outperforms all baselines by up to 44% in 3D chamfer distance 🚀.
June 6, 2025 at 1:52 PM
We train RaySt3R by curating a new dataset, for a total of 12 million views 📷 with Objaverse and GSO objects. The ablations 🔍 suggest that more and more diverse data improves RaySt3R's performance. RaySt3R does not require GT meshes, paving the way for training on real-world data.
June 6, 2025 at 1:52 PM
💡 Our key insight is that 3D object shape completion can be recasted as a novel-view synthesis problem. RaySt3R takes a masked RGB-D image as input, and predicts depth maps and object masks for novel views. We query multiple views and merge the predictions into a consistent point cloud.
June 6, 2025 at 1:52 PM
We focus on multi-object 3D shape completion for robotics. Robots are commonly equipped with a RGB-D camera 📷, but their measurements are noisy and incomplete.

Using only DINOv2 features 🦖 as pretraining, we train a new model (RaySt3R) to produce accurate geometry.
June 6, 2025 at 1:51 PM
Do you think Europe will take the opportunity? The Netherlands is even cutting research funds under the new administration... It feels like there are still significantly more opportunities in the US.
March 26, 2025 at 3:24 PM
Thanks Chris! This was a push with the entire dust3r team @naverlabseurope.bsky.social, congrats everyone!
March 26, 2025 at 3:15 PM
😆
January 13, 2025 at 2:17 PM
Is the book just as good/better than the show for "The 3 body problem"?
December 5, 2024 at 9:58 AM
For international students: renewing your visa asap might be a good idea.
November 22, 2024 at 8:14 PM