UQ-Loc: Uncertainty-Aware LiDAR Scene Coordinate Regression

2026-08-06 · 17:23 UTC ·Jacek Komorowski ·1 min read

Brief

UQ-Loc addresses aleatoric uncertainty in LiDAR-based Scene Coordinate Regression by adding an anisotropic Gaussian covariance head to LightLoc, predicting per-voxel 3x3 positive-definite covariances. Training uses NLL plus a kNN spatial smoothness term; inference applies an uncertainty-weighted SC2-PCR and Mahalanobis inlier test. Experiments (abstract only) claim consistent 6-DoF accuracy gains and well-calibrated covariances.

Why it matters

UQ-Loc (Jacek Komorowski, published 2026-08-06) extends the LightLoc LiDAR Scene Coordinate Regression architecture with an anisotropic Gaussian covariance head that predicts a full 3x3 positive-definite covariance matrix per voxel, trained with a Negative Log-Likelihood (NLL) loss plus a kNN-based spatial smoothness regulariser.

Key details

Inference uses a modified SC2-PCR solver with uncertainty-weighted seed scoring and a Mahalanobis-distance inlier test; the paper adopts Expected Calibration Error (ECE) to evaluate uncertainty quality and reports consistent improvements in 6-DoF localization accuracy along with well-calibrated covariances.

Abstract

LiDAR-based Scene Coordinate Regression (SCR) maps point clouds directly to 3D scene coordinates, enabling precise 6-DoF localisation without explicit map retrieval. However, existing methods produce deterministic predictions, discarding aleatoric uncertainty that could improve robustness and downstream decision-making. We present UQ-Loc, which extends the LightLoc architecture with an anisotropic Gaussian covariance head that predicts a full 3x3 positive-definite covariance matrix per voxel. Training uses a Negative Log-Likelihood (NLL) loss augmented with a kNN-based spatial smoothness regulariser, while inference employs a modified SC2-PCR solver with uncertainty-weighted seed scoring and a Mahalanobis-distance inlier test. We adopt Expected Calibration Error (ECE) as a principled metric for evaluating the quality of the predicted uncertainty. Experiments demonstrate that UQ-Loc achieves consistent improvement in 6-DoF localization accuracy while producing well-calibrated covariances.