5 papers
Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection
Kaiqiang Li, Gang Li, Mingle Zhou +3
Zero-shot (ZS) 3D anomaly detection is crucial for reliable industrial inspection, as it enables detecting and localizing defects without requiring any target-category training dat…
PD-4DGS:Progressive Decomposition of 4D Gaussian Splatting for Bandwidth-Adaptive Dynamic Scene Streaming
Jiachen Li, Guangzhi Han, Jin Wan +5
4D Gaussian Splatting (4DGS) enables high-quality dynamic novel view synthesis, yet current models remain monolithic bitstreams that clients must download in full before any frame…
Multimodal Industrial Anomaly Detection via Geometric Prior
Min Li, Jinghui He, Gang Li +3
The purpose of multimodal industrial anomaly detection is to detect complex geometric shape defects such as subtle surface deformations and irregular contours that are difficult to…
Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection
Mingle Zhou, Jiahui Liu, Jin Wan +2
Unsupervised Continuous Anomaly Detection (UCAD) is gaining attention for effectively addressing the catastrophic forgetting and heavy computational burden issues in traditional Un…
MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detection
Gang Li, Tianjiao Chen, Mingle Zhou +3
Zero-shot 3D (ZS-3D) anomaly detection aims to identify defects in 3D objects without relying on labeled training data, making it especially valuable in scenarios constrained by da…