Yuqiu Liu — accepted AI-conference papers

2026

FluidGaussian: Propagating Simulation-Based Uncertainty Toward Functionally-Intelligent 3D Reconstruction

Yuqiu Liu, Jialin Song, Marissa Ramirez de Chanlatte, Rochishnu Chowdhury, Rushil Paresh Desai, Wuyang Chen +2

CVPR 2026

Real objects that inhabit the physical world follow physical laws and thus behave plausibly during interaction with other physical objects. However, current methods that perform 3D reconstructions of real-world scenes from multi-view 2D images optimize primarily for visual fidelity, i.e., they train

Cited by 0 Source ↗code ↗Scholar ↗

2024

Full-Distance Evasion of Pedestrian Detectors in the Physical World

Zhi Cheng, Zhanhao Hu, Yuqiu Liu, Jianmin Li, Hang Su, Xiaolin Hu

NeurIPS 2024poster

Many studies have proposed attack methods to generate adversarial patterns for evading pedestrian detection, alarming the computer vision community about the need for more attention to the robustness of detectors. However, adversarial patterns optimized by these methods commonly have limited perform…

Cited by 1 Source ↗PDF ↗code ↗Scholar ↗

2024

Infrared Adversarial Car Stickers

Xiaopei Zhu, Yuqiu Liu, Zhanhao Hu, Jianmin Li, Xiaolin Hu

CVPR 2024poster

Infrared physical adversarial examples are of great significance for studying the security of infrared AI systems that are widely used in our lives such as autonomous driving. Previous infrared physical attacks mainly focused on 2D infrared pedestrian detection which may not fully manifest its destr…

Cited by 3 Source ↗PDF ↗Scholar ↗