Abstract
We present MVIP, a novel dataset for multi-modal and multi-view application oriented industrial part recognition. Here we combine a calibrated RGBD multi-view dataset with additional object context such as physical properties, natural language, and super-classes. Our main goal with MVIP is to study and push transferability of various state-of-the-art methods within related downstream tasks towards an efficient deployment of industrial classifiers. Additionally, we intent to push with MVIP research regarding several modality fusion topics, (automated) synthetic data generation, and complex data sampling methods -- combined in a single application oriented benchmark.
