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SLAM · State Estimation · Autonomous Driving

Mapping, localizing,
learning, writing.

I'm Yu Zhang. I work on autonomous driving algorithms, going deep in SLAM and robotics. I enjoy exploring all kinds of things, and I like writing down how I grow and think along the way.

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fig. pose graph, 42 keyframes

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GAVIS overview: anisotropic visibility field quantifies uncertainty by modeling which regions are observed by training views. Left room (visible) → low uncertainty; right room (invisible) → high uncertainty in the rendered uncertainty map.

fig. GAVIS overview: anisotropic visibility field quantifies uncertainty by modeling which regions are observed by training views. Left room (visible) → low uncertainty; right room (invisible) → high uncertainty in the rendered uncertainty map.

K2E-B-G2-7 · PAPER NOTE

GAVIS: Anisotropic Visibility Field for Uncertainty-Driven 3DGS Active Mapping

GAVIS paper note — uncertainty quantification for 3DGS via a per-particle anisotropic visibility field; spherical harmonics representation, 200+ FPS real-time UQ, outperforms FisherRF/VIMC/NVF across all image-quality metrics

2026-07-13 · slam / papers

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