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. 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

fig. Uncertainty maps for an unconverged vs. converged 3DGS scene: rendered image, render error, and estimated color/semantic uncertainty. Estimated uncertainty closely tracks true render error in both regimes.
K2E-B-G6-6
These Magic Moments: Differentiable Uncertainty Quantification of Radiance Field Models
2026-07-13 · slam / papers

fig. Per-pixel uncertainty estimation for novel view synthesis: ground truth, RGB render, predicted uncertainty map, and true error (DSSIM). The post-hoc uncertainty map closely mirrors regions of actual rendering error.
K2E-B-G6-7
3DGS-U: Predictive Photometric Uncertainty in Gaussian Splatting
2026-07-13 · slam / papers
Recent Notes
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Thoughts
View all →Reflections on career, technology, and engineering culture.