Similarly, we plot the distribution of number of matched points and cumulative curve after 3️⃣, helping to identify the top quantile of well-calibrated camera pairs within each lab.

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Automatically calibrating a large-scale dataset is challenging. We provide quality assessment metrics across all three stages, with flexibility to narrow bounds for downstream tasks as needed.

1️⃣ and 2️⃣ quality metrics show IOU and Reprojection-error distributions post-calibration.

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Below we show the Camera-to-Camera transformations, post-calibration improves the alignment of obtained pointclouds!

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Introducing ✨Posed DROID✨, results of our efforts at automatic post-hoc calibration of a large-scale robotics manipulation dataset.

Try it out at: droid-dataset.github.io

Learn more at:
🌐 arXiv: arxiv.org/pdf/2403.12945
📄 Blog: medium.com/p/4ddfc45361d3

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🚀Exciting News! Join us at the inaugural #CVPR2025 Workshop on 3D Vision Language Models (VLMs) for Robotics Manipulation on June 11, 2025, in Nashville, TN! 🦾

robo-3dvlms.github.io

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@cvprconference.bsky.social

It's a great tool created by Andreas Geiger and co. (not here yet). Among many other things, I really like the daily digest email it sents out every evening, also you could upvote certain papers to tune the algorithm to your preference better.

How do we represent 3D world knowledge for spatial intelligence in next-generation robots? We recently wrote an extensive survey paper on this emerging topic, covering recent state-of-the-art! 🦾 🚀
Check it out below. Feedback/Suggestions welcome!

📖arXiv: arxiv.org/abs/2410.20220