Eric Brachmann

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Directory member with public evidence across AI research, Vision & synthetic media, Compute & infrastructure.

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Niantic Spatial, Research. Throws machine learning at traditional computer vision pipelines to see what sticks. Differentiates the non-differentiable. 📍Europe 🔗 http://ebrach.github.io

Articles & links

New #ECCV2026 paper alert: 🧭 AutoCompass: Accurate Visual Localization on Public Maps by Learning from Weak Labels 🧭 Paper: arxiv.org/abs/2609.02798 Website: nianticspatial.github.io/autocompass/ Video: www.youtube.com/watch?v=hmUF...

AutoCompass: Accurate Visual Localization on Public Maps by Learning from Weak Labels arxiv.org
AI Weekly's analysis
  • AutoCompass trains neural map matchers using only raw GPS labels and shows heading labels are unnecessary because models learn heading automatically.
  • The method adds a tolerance region around each noisy GPS point and, when available, uses relative poses from SLAM or SfM as a stronger signal.
  • The authors report gains over absolute-pose-supervised baselines on both driving and egocentric localization benchmarks.
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Recent commentary

I think I am ready to be replaced by AI. My whole mentoring can be summarized by the following expert system: if True: print("Visualize everything.")

View on Bluesky · ♥ 11 ↻ 0 ↩ 1 · 99d ago

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