Eric Brachmann

Eric Brachmann

@ericbrachmann.bsky.social

Niantic Spatial, Research. Throws machine learning at traditional computer vision pipelines to see what sticks. Differentiates the non-differentiable. ๐Ÿ“Europe ๐Ÿ”— http://ebrach.github.io

2K followers588 following496 posts

Eric Brachmann reposted

Interested in doing a PhD in machine learning or computer vision at the University of Edinburgh? My group works on topics related to: * Autonomous Scientific Discovery * Human-in-the-Loop Machine Learning * 3D Vision and Robotics * AI for Ecology and Conservation

It appears that coding agents have been trained on too many SOTA chasing papers, because they latch onto tiny, meaningless improvements in metrics. That's what you get...

What fresh hell is this? Scholar inbox now comes with 2 min podcasts of each paper... I randomly choose two relocalization papers and they both started with "Imagine navigating a large, dark warehouse" ... No - I do not want to imagine this. Was this trained on YouTube slasher flicks? Why "dark"?!

Eric Brachmann reposted

๐Ÿš€We have PhD openings in my lab! 3D got pretty interesting :) Generation is getting better, which makes real scenes more interesting: things get used, opened, moved, etc We want to understand that in 3D: function, dynamics, physics ๐Ÿ‘‰apply 3dunderstanding.org/openings.html or via ELLIS

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We will present the paper at hashtag #ECCV2026: #116 at 10:30 AM on Sep 11 (Friday) in Poster Session 3. If you prefer a more intimate setting, we will also present it as an invited poster at the Instance-Level Recognition and Generation Workshop, 10:00 AM on Sep 8 (Tuesday).

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So we train with weak supervision instead. The result is a set of losses catering to different training settings: Got images with GPS but no heading? Check. Relative poses but not scale-metric? Sure. Scale-metric relative poses? Great, bring them. Even coarsely geo-aligned? We have a loss for that.

The headline numbers of AutoCompass are just amazing and set a new SOTA for this task. By switching to weak supervision, you beat associated baselines. Also some interesting findings: train on coarse position without heading labels only, and accurate heading prediction just comes as a side product.

Need an accurate camera pose, but all you have is an image and a coarse location, e.g. from GPS? No problem, just localize against OpenStreetMap. Methods unlocking that capability are called Neural Map Matchers which had poor accuracy so far. But we found the problem.

To train these systems, you need many examples of images with geo-located pose annotations. But the ground truth of existing datasets is unreliable and often imprecise. At the scale required for training, better ground truth is difficult to obtain.

I have had 70 co-authors so far. I took some time to wire all these names up with their websites on my personal page. (The BOP papers have been a real pain. ๐Ÿ˜…) Fun to browse through what all these people have been up to since we collaborated.

Eric Brachmann reposted

We are grateful to all of the 17,491 reviewers who helped make #CVPR2026 possible. We are especially pleased to recognize the following Outstanding Reviewers, whose high-quality reviews (as judged by their Area Chairs) placed them among the top 5% of reviewers. 1/2

We were pretty mad at the reviewer but they turned out to be reasonable. I understand the frustration that classical methods are often overlooked. Here, it was a genuine oversight. I did not know the E5+1 solver. Including it made the paper stronger. Full discussion: openreview.net/forum?id=rmD...

openreview.netA Scene is Worth a Thousand Features: Feed-Forward Camera...Visually localizing an image, i.e., estimating its camera pose, requires building a scene representation that serves as a visual map. The representation we choose has direct consequences towards...
David Nordstrรถm@davnords.bsky.social

Side note: pretty cool you managed to get a reviewer to update from 2 to 8 after a strong rebuttal. Will live on that hopium for ECCV rebuttals...

FastForward has been accepted to #ICLR2026 and will be presented there next week. If you go, say hi to @axelbarroso.bsky.social. If not, don't fret. Axel has prepared a short video for you: www.youtube.com/watch?v=r0Qk...

youtube.com[ICLR 2026] Feed-Forward Camera Localization from a Collection of Image FeaturesYouTube video by Niantic Spatial
Eric Brachmann@ericbrachmann.bsky.social

โฉ FastForward โฉ A new model for efficient visual relocalization. Accurate camera poses without building structured 3D maps. nianticspatial.github.io/fastforward/ Work by @axelbarroso.bsky.social, Tommaso Cavallari and Victor Adrian Prisacariu.

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Vision and Graphics Trends@si-cv-graphics.bsky.social

๐—”๐—ป๐˜† ๐Ÿฏ๐—— ๐—ฆ๐—ฐ๐—ฒ๐—ป๐—ฒ ๐—ถ๐˜€ ๐—ช๐—ผ๐—ฟ๐˜๐—ต ๐Ÿญ๐—ž ๐—ง๐—ผ๐—ธ๐—ฒ๐—ป๐˜€: ๐Ÿฏ๐——-๐—š๐—ฟ๐—ผ๐˜‚๐—ป๐—ฑ๐—ฒ๐—ฑ ๐—ฅ๐—ฒ๐—ฝ๐—ฟ๐—ฒ๐˜€๐—ฒ๐—ป๐˜๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ณ๐—ผ๐—ฟ ๐—ฆ๐—ฐ๐—ฒ๐—ป๐—ฒ ๐—š๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ฎ๐˜ ๐—ฆ๐—ฐ๐—ฎ๐—น๐—ฒ Dongxu Wei, Qi Xu, Zhiqi Li ... Peidong Liu arxiv.org/abs/2604.11331 Trending on www.scholar-inbox.com

Thanks, but no thanks. You might get me after all if we submit, but for now it feels good to decline. Last year, it was a horrible experience to review for NeurIPS. And I had a reduced load of two papers....

Eric Brachmann@ericbrachmann.bsky.social

Interesting, you are right, the general process did not change in the last few years. I did not review for NeurIPS in a long time. I volunteered this year but would not do it again in the current setting. It is an unreasonable amount of work compared to eg *CCV/CVPR. And ACing must be a nightmare.

If anything, I suspect that these tools will cover holes with persuasive bla bla. I do not think that authors are doing themselves a favour in the long run. (sorry for quote posting, not allowed to answer...)

Thomas Dietterich@tdietterich.bsky.social

For me, writing is the process of assembling a logical narrative, and it is during the writing process that I discover the holes in my reasoning. I'm very skeptical that current AI tools are capable of doing this.

Interviews for our 2026 research internships will commence soon. It is not too late to apply! A lot of exciting stuff is happening in 3D vision, let's explore it together.

Eric Brachmann@ericbrachmann.bsky.social

You know what season it is! Right. Internship application season. Niantic Spatial is offering research internships on a multitude of 3D vision topics: relocalization, reconstruction, 3D VLMs... Top tier papers regularly come out of our internships ๐Ÿš€ nianticspatial.careers.hibob.com/jobs/0fc4871...

Eric Brachmann reposted

Robotics researchers, this is an occasion not to be missed: top-notch speakers in an excellent location in the Alps this February ๐Ÿค–โ›ท๏ธ It's the Winter School on Social Robotics, Artificial Intelligence and Multimedia (SoRAIM), 9-13 Feb 2026 ๐Ÿ‘‡

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