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We present a novel alignment-before-generation approach to tackle the challenging task of generating general 3D shapes based on 2D images or texts.

Machine learning models are increasingly being deployed in real-world contexts. However, systematic studies on their transferability to specific and critical applications are underrepresented in the research literature. An important example is visual anomaly detection (V AD) for robotic power line inspection.

Typically, hashing methods are essential for the use of Jaccard similarity tobepractical inlarge-scale settings.