14 citations · 55 across the 23 of their papers we have counts for
9 papers · 1 filter
XYZCylinder: Towards Compatible Feed-Forward 3D Gaussian Splatting for Driving Scenes via Unified Cylinder Lifting Method
Haochen Yu, Qiankun Liu, Hongyuan Liu +4
Feed-forward paradigms for 3D reconstruction have become a focus of recent research, which learn implicit, fixed view transformations to generate a single scene representation. How…
MVSMamba: Multi-View Stereo with State Space Model
Jianfei Jiang, Qiankun Liu, Hongyuan Liu +4
Robust feature representations are essential for learning-based Multi-View Stereo (MVS), which relies on accurate feature matching. Recent MVS methods leverage Transformers to capt…
InstDrive: Instance-Aware 3D Gaussian Splatting for Driving Scenes
Hongyuan Liu, Haochen Yu, Bochao Zou +4
Reconstructing dynamic driving scenes from dashcam videos has attracted increasing attention due to its significance in autonomous driving and scene understanding. While recent adv…
MonoMVSNet: Monocular Priors Guided Multi-View Stereo Network
Jianfei Jiang, Qiankun Liu, Haochen Yu +4
Learning-based Multi-View Stereo (MVS) methods aim to predict depth maps for a sequence of calibrated images to recover dense point clouds. However, existing MVS methods often stru…
Kaleidoscopic Background Attack: Disrupting Pose Estimation with Multi-Fold Radial Symmetry Textures
Xinlong Ding, Hongwei Yu, Jiawei Li +5
Camera pose estimation is a fundamental computer vision task that is essential for applications like visual localization and multi-view stereo reconstruction. In the object-centric…
From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
Xinyang Li, Siqi Liu, Bochao Zou +2
As large language models evolve, there is growing anticipation that they will emulate human-like Theory of Mind (ToM) to assist with routine tasks. However, existing methods for ev…
AGC-Drive: A Large-Scale Dataset for Real-World Aerial-Ground Collaboration in Driving Scenarios
Yunhao Hou, Bochao Zou, Min Zhang +7
By sharing information across multiple agents, collaborative perception helps autonomous vehicles mitigate occlusions and improve overall perception accuracy. While most previous w…
DeCoDe: Defer-and-Complement Decision-Making via Decoupled Concept Bottleneck Models
Chengbo He, Bochao Zou, Junliang Xing +3
In human-AI collaboration, a central challenge is deciding whether the AI should handle a task, be deferred to a human expert, or be addressed through collaborative effort. Existin…
Enhancing LLM Reasoning with Multi-Path Collaborative Reactive and Reflection agents
Chengbo He, Bochao Zou, Xin Li +3
Agents have demonstrated their potential in scientific reasoning tasks through large language models. However, they often face challenges such as insufficient accuracy and degenera…