219 citations · 237 across the 8 of their papers we have counts for
8 papers · 1 filter
ZeroBP: Learning Position-Aware Correspondence for Zero-shot 6D Pose Estimation in Bin-Picking
Jianqiu Chen, Zikun Zhou, Xin Li +3
Bin-picking is a practical and challenging robotic manipulation task, where accurate 6D pose estimation plays a pivotal role. The workpieces in bin-picking are typically textureles…
MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection
Tianpeng Bao, Jiadong Chen, Wei Li +5
Visual anomaly detection plays a crucial role in not only manufacturing inspection to find defects of products during manufacturing processes, but also maintenance inspection to ke…
Geo6D: Geometric Constraints Learning for 6D Pose Estimation
Jianqiu Chen, Mingshan Sun, Ye Zheng +7
Numerous 6D pose estimation methods have been proposed that employ end-to-end regression to directly estimate the target pose parameters. Since the visible features of objects are…
Uni6Dv2: Noise Elimination for 6D Pose Estimation
Mingshan Sun, Ye Zheng, Tianpeng Bao +5
Uni6D is the first 6D pose estimation approach to employ a unified backbone network to extract features from both RGB and depth images. We discover that the principal reasons of Un…
Benchmarking Unsupervised Anomaly Detection and Localization
Ye Zheng, Xiang Wang, Yu Qi +2
Unsupervised anomaly detection and localization, as of one the most practical and challenging problems in computer vision, has received great attention in recent years. From the ti…
Uni6D: A Unified CNN Framework without Projection Breakdown for 6D Pose Estimation
Xiaoke Jiang, Donghai Li, Hao Chen +3
As RGB-D sensors become more affordable, using RGB-D images to obtain high-accuracy 6D pose estimation results becomes a better option. State-of-the-art approaches typically use di…
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
Jiawei Yu, Ye Zheng, Xiang Wang +4
Unsupervised anomaly detection and localization is crucial to the practical application when collecting and labeling sufficient anomaly data is infeasible. Most existing representa…
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization
Ye Zheng, Xiang Wang, Rui Deng +3
The essence of unsupervised anomaly detection is to learn the compact distribution of normal samples and detect outliers as anomalies in testing. Meanwhile, the anomalies in real-w…