collaborators

14 papers

cs.CV2026

ConFixGS: Learning to Fix Feedforward 3D Gaussian Splatting with Confidence-Aware Diffusion Priors in Driving Scenes

Rui Song, Tianhui Cai, Markus Gross +5

Feedforward 3D Gaussian Splatting (3DGS) often struggles in trajectory-based sparse-view driving scenes. Existing Gaussian repair methods mainly target optimization-based 3DGS, whi…

cs.CV2026

CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs

Xingcheng Zhou, Hao Guo, Rui Song +5

Safety-critical traffic reasoning requires contrastive consistency: models must detect true hazards when an accident occurs, and reliably reject plausible-but-false hypotheses unde…

cs.RO2026

OVPD: A Virtual-Physical Fusion Testing Dataset of OnSite Auton-omous Driving Challenge

Yuhang Zhang, Jiarui Zhang, Bowen Jian +6

The rapid iteration of autonomous driving algorithms has created a growing demand for high-fidelity, replayable, and diagnosable testing data. However, many public datasets lack re…

cs.CV2026

PointTPA: Dynamic Network Parameter Adaptation for 3D Scene Understanding

Siyuan Liu, Chaoqun Zheng, Xin Zhou +3

Scene-level point cloud understanding remains challenging due to diverse geometries, imbalanced category distributions, and highly varied spatial layouts. Existing methods improve…

cs.CV2026

SGTA: Scene-Graph Based Multi-Modal Traffic Agent for Video Understanding

Xingcheng Zhou, Mingyu Liu, Walter Zimmer +2

We present Scene-Graph Based Multi-Modal Traffic Agent (SGTA), a modular framework for traffic video understanding that combines structured scene graphs with multi-modal reasoning.…

cs.CV2026

SegRGB-X: General RGB-X Semantic Segmentation Model

Jiong Liu, Yingjie Xu, Xingcheng Zhou +4

Semantic segmentation across arbitrary sensor modalities faces significant challenges due to diverse sensor characteristics, and the traditional configurations for this task result…