13 citations · 31 across the 23 of their papers we have counts for
21 papers · 1 filter
One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models
Xiaohao Xu, Feng Xue, Xiang Li +5
A faithful 3D world representation should account for layered geometry, where a single camera ray may contain multiple visible and geometrically valid surfaces. Monocular depth est…
Probing Collision Grounding in Vision-Language Models for Safe Human-Robot Collaboration
Jun Wang, Xiaohao Xu, Xiaonan Huang
Safe human--robot collaboration requires more than visual description: a monitor must determine whether the robot body is safely separated, already colliding with the scene or a pe…
The 3D Mirage: Probing and Taming 3D Hallucinations
Hoang Nguyen, Xiaohao Xu, Xiaonan Huang
Monocular depth foundation models achieve remarkable generalization by learning large-scale semantic priors, but this creates a critical vulnerability: they hallucinate illusory 3D…
Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark
Yunkang Cao, Yuqi Cheng, Xiaohao Xu +6
The practical deployment of Visual Anomaly Detection (VAD) systems is hindered by their sensitivity to real-world imaging variations, particularly the complex interplay between vie…
Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation
Bozhong Zheng, Jinye Gan, Xiaohao Xu +5
3D point cloud anomaly detection is essential for robust vision systems but is challenged by pose variations and complex geometric anomalies. Existing patch-based methods often suf…
Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detection
Wenqiao Li, Yao Gu, Xintao Chen +4
Humans detect real-world object anomalies by perceiving, interacting, and reasoning based on object-conditioned physical knowledge. The long-term goal of Industrial Anomaly Detecti…
Towards Ambiguity-Free Spatial Foundation Model: Rethinking and Decoupling Depth Ambiguity
Xiaohao Xu, Feng Xue, Xiang Li +5
Depth ambiguity is a fundamental challenge in spatial scene understanding, especially in transparent scenes where single-depth estimates fail to capture full 3D structure. Existing…
Scalable Benchmarking and Robust Learning for Noise-Free Ego-Motion and 3D Reconstruction from Noisy Video
Xiaohao Xu, Tianyi Zhang, Shibo Zhao +8
We aim to redefine robust ego-motion estimation and photorealistic 3D reconstruction by addressing a critical limitation: the reliance on noise-free data in existing models. While…
Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties
Wenqiao Li, Bozhong Zheng, Xiaohao Xu +8
Object anomaly detection is essential for industrial quality inspection, yet traditional single-sensor methods face critical limitations. They fail to capture the wide range of ano…
MAC-Ego3D: Multi-Agent Gaussian Consensus for Real-Time Collaborative Ego-Motion and Photorealistic 3D Reconstruction
Xiaohao Xu, Feng Xue, Shibo Zhao +3
Real-time multi-agent collaboration for ego-motion estimation and high-fidelity 3D reconstruction is vital for scalable spatial intelligence. However, traditional methods produce s…
Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity
Huaxin Zhang, Xiaohao Xu, Xiang Wang +6
How can we enable models to comprehend video anomalies occurring over varying temporal scales and contexts? Traditional Video Anomaly Understanding (VAU) methods focus on frame-lev…
UniDrive: Towards Universal Driving Perception Across Camera Configurations
Ye Li, Wenzhao Zheng, Xiaonan Huang +1
Vision-centric autonomous driving has demonstrated excellent performance with economical sensors. As the fundamental step, 3D perception aims to infer 3D information from 2D images…
Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM
Huaxin Zhang, Xiaohao Xu, Xiang Wang +6
Towards open-ended Video Anomaly Detection (VAD), existing methods often exhibit biased detection when faced with challenging or unseen events and lack interpretability. To address…
From Perfect to Noisy World Simulation: Customizable Embodied Multi-modal Perturbations for SLAM Robustness Benchmarking
Xiaohao Xu, Tianyi Zhang, Sibo Wang +6
Embodied agents require robust navigation systems to operate in unstructured environments, making the robustness of Simultaneous Localization and Mapping (SLAM) models critical to…
Learning Shared RGB-D Fields: Unified Self-supervised Pre-training for Label-efficient LiDAR-Camera 3D Perception
Xiaohao Xu, Ye Li, Tianyi Zhang +3
Constructing large-scale labeled datasets for multi-modal perception model training in autonomous driving presents significant challenges. This has motivated the development of sel…
GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection
Huaxin Zhang, Xiang Wang, Xiaohao Xu +6
In recent years, video anomaly detection has been extensively investigated in both unsupervised and weakly supervised settings to alleviate costly temporal labeling. Despite signif…
-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations
Xiang Li, Kai Qiu, Jinglu Wang +6
Referring perception, which aims at grounding visual objects with multimodal referring guidance, is essential for bridging the gap between humans, who provide instructions, and the…
Is Your LiDAR Placement Optimized for 3D Scene Understanding?
Ye Li, Lingdong Kong, Hanjiang Hu +2
The reliability of driving perception systems under unprecedented conditions is crucial for practical usage. Latest advancements have prompted increasing interest in multi-LiDAR pe…
Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning
Xiaohao Xu, Yunkang Cao, Huaxin Zhang +2
Anomaly detection is vital in various industrial scenarios, including the identification of unusual patterns in production lines and the detection of manufacturing defects for qual…
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect
Yunkang Cao, Xiaohao Xu, Jiangning Zhang +4
Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e.g., industrial defect inspect…
Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead
Yunkang Cao, Xiaohao Xu, Chen Sun +2
Anomaly detection is a crucial task across different domains and data types. However, existing anomaly detection models are often designed for specific domains and modalities. This…