arXiv subjects

Qun Wu

Publications and source records attributed to Qun Wu.

3 recordsLinked to original sources

arXiv2026

Biases in the Determination of Correlations Between Underground Muon Flux and Atmospheric Temperature

The underground rates of cosmic-ray muons exhibit seasonal variations correlated with effective atmospheric temperature, quantified via a single coefficient. We compare two analysis methods for studying the correlation: the standard Unbinned Method, where all rate-temperature data points are fit simultaneously via linear regression, and the Binned Method, where data points with similar temperatures are first grouped into bins before fitting. We find that while both methods are unbiased in the limit of negligible temperature uncertainties, the Binned Method develops significant bias when temperature uncertainties are present, due to binning-induced distortions. In contrast, the Unbinned Method remains robust if the uncertainties are accurately known. To address the widely encountered issue of imprecise uncertainty estimation, we propose a novel procedure that assesses correlation stability by varying the time intervals and their assigned uncertainties. This approach resolves methodological tensions in studies of seasonal modulation of the muon rate and provides a practical framework for robust correlation estimation under real-world conditions.

hep-ex↗

arXiv2025

CuriGS: Curriculum-Guided Gaussian Splatting for Sparse View Synthesis

3D Gaussian Splatting (3DGS) has recently emerged as an efficient, high-fidelity representation for real-time scene reconstruction and rendering. However, extending 3DGS to sparse-view settings remains challenging because of supervision scarcity and overfitting caused by limited viewpoint coverage. In this paper, we present CuriGS, a curriculum-guided framework for sparse-view 3D reconstruction using 3DGS. CuriGS addresses the core challenge of sparse-view synthesis by introducing student views: pseudo-views sampled around ground-truth poses (teacher). For each teacher, we generate multiple groups of student views with different perturbation levels. During training, we follow a curriculum schedule that gradually unlocks higher perturbation level, randomly sampling candidate students from the active level to assist training. Each sampled student is regularized via depth-correlation and co-regularization, and evaluated using a multi-signal metric that combines SSIM, LPIPS, and an image-quality measure. For every teacher and perturbation level, we periodically retain the best-performing students and promote those that satisfy a predefined quality threshold to the training set, resulting in a stable augmentation of sparse training views. Experimental results show that CuriGS outperforms state-of-the-art baselines in both rendering fidelity and geometric consistency across various synthetic and real sparse-view scenes. Project page: https://zijian1026.github.io/CuriGS/

cs.CV↗

arXiv2025

The Fourth Monocular Depth Estimation Challenge

This paper presents the results of the fourth edition of the Monocular Depth Estimation Challenge (MDEC), which focuses on zero-shot generalization to the SYNS-Patches benchmark, a dataset featuring challenging environments in both natural and indoor settings. In this edition, we revised the evaluation protocol to use least-squares alignment with two degrees of freedom to support disparity and affine-invariant predictions. We also revised the baselines and included popular off-the-shelf methods: Depth Anything v2 and Marigold. The challenge received a total of 24 submissions that outperformed the baselines on the test set; 10 of these included a report describing their approach, with most leading methods relying on affine-invariant predictions. The challenge winners improved the 3D F-Score over the previous edition's best result, raising it from 22.58% to 23.05%.

cs.CV↗