arXiv subjects

Changhe Liu

Publications and source records attributed to Changhe Liu.

3 recordsLinked to original sources

arXiv2026

NTIRE 2026 3D Restoration and Reconstruction in Real-world Adverse Conditions: RealX3D Challenge Results

This paper presents a comprehensive review of the NTIRE 2026 3D Restoration and Reconstruction (3DRR) Challenge, detailing the proposed methods and results. The challenge seeks to identify robust reconstruction pipelines that are robust under real-world adverse conditions, specifically extreme low-light and smoke-degraded environments, as captured by our RealX3D benchmark. A total of 279 participants registered for the competition, of whom 33 teams submitted valid results. We thoroughly evaluate the submitted approaches against state-of-the-art baselines, revealing significant progress in 3D reconstruction under adverse conditions. Our analysis highlights shared design principles among top-performing methods and provides insights into effective strategies for handling 3D scene degradation.

cs.CV↗

arXiv2025

UTrice: Unifying Primitives in Differentiable Ray Tracing and Rasterization via Triangles for Particle-Based 3D Scenes

Ray tracing 3D Gaussian particles enables realistic effects such as depth of field, refractions, and flexible camera modeling for novel-view synthesis. However, existing methods trace Gaussians through proxy geometry, which requires constructing complex intermediate meshes and performing costly intersection tests. This limitation arises because Gaussian-based particles are not well suited as unified primitives for both ray tracing and rasterization. In this work, we propose a differentiable triangle-based ray tracing pipeline that directly treats triangles as rendering primitives without relying on any proxy geometry. Our results show that the proposed method achieves significantly higher rendering quality than existing ray tracing approaches while maintaining real-time rendering performance. Moreover, our pipeline can directly render triangles optimized by the rasterization-based method Triangle Splatting, thus unifying the primitives used in novel-view synthesis.

cs.CV↗

arXiv2025

Latent Class Logit Kernel Framework for Surrogate Safety: Identifying Behavioural Thresholds through Conflict Indicator Profiles

Crash data objectively characterize road safety but are rare and often unsuitable for proactive safety management. Traffic conflict indicators such as time-to-collision (TTC) provide continuous measures of collision proximity but require thresholds to distinguish routine from safety-critical interactions. Extreme Value Theory (EVT) offers statistically defined thresholds, yet these do not necessarily represent how drivers perceive and respond to conflict. This study introduces a behavioural modelling framework that identifies candidate behavioural thresholds (CBTs) by explicitly modelling how drivers adjust their movements under conflict conditions. The framework is based on a Latent Class Logit Kernel (LC-LK) model that captures inter-class heterogeneity (routine vs. defensive driving) and intra-class correlation between overlapping spatial alternatives. This yields probability curves showing how the likelihood of defensive manoeuvres varies with conflict indicators, from which CBTs such as inflection points and crossovers can be extracted. The framework tests four hypotheses: (1) drivers exhibit varying degrees of membership in both low- and high-risk classes; (2) membership shifts systematically with conflict values, revealing behavioural thresholds; (3) this relationship follows a logistic shape, with stable behaviour at safe levels and rapid transitions near critical points; and (4) even in free flow, drivers maintain a baseline caution level. Application to naturalistic roundabout trajectories revealed stable TTC thresholds (0.8-1.1 s) but unstable MTTC2 estimates (e.g., 34 s), suggesting cognitive limits in processing complex indicators. Overall, the framework complements EVT by offering a structured, behaviourally grounded method for identifying and validating thresholds in surrogate safety analysis.

physics.soc-ph↗