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Jianfeng Yang

2 accepted papers

2026

Multi-Prototype Compactness and Boundary-Aware Synthesis for Unsupervised Anomaly Detection

CVPR 2026

Unsupervised Anomaly Detection (UAD) is crucial for industrial quality control. Many existing embedding-based methods adopt a single-prototype assumption and learn, for example, a compact hypersphere to enclose all normal features. However, this strategy breaks down under intra-class variance caused

Cited by 0SourceScholar
2024

CMG-Net: Robust Normal Estimation for Point Clouds via Chamfer Normal Distance and Multi-Scale Geometry

AAAI 2024technical

This work presents an accurate and robust method for estimating normals from point clouds. In contrast to predecessor approaches that minimize the deviations between the annotated and the predicted normals directly, leading to direction inconsistency, we first propose a new metric termed Chamfer Nor…