mlsanityData to 7 Oct 2026

MVIP -- A Dataset and Methods for Application Oriented Multi-View and Multi-Modal Industrial Part Recognition

arXiv 2502.15448 ↗First posted 21 Feb 2025cs.CV cs.AIcited by 3

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Abstract (arXiv)

We present MVIP, a novel dataset for multi-modal and multi-view application-oriented industrial part recognition. Here we are the first to combine a calibrated RGBD multi-view dataset with additional object context such as physical properties, natural language, and super-classes. The current portfolio of available datasets offers a wide range of representations to design and benchmark related methods. In contrast to existing classification challenges, industrial recognition applications offer controlled multi-modal environments but at the same time have different problems than traditional 2D/3D classification challenges. Frequently, industrial applications must deal with a small amount or increased number of training data, visually similar parts, and varying object sizes, while requiring a robust near 100% top 5 accuracy under cost and time constraints. Current methods tackle such challenges individually, but direct adoption of these methods within industrial applications is complex and requires further research. 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 intend to push with MVIP research regarding several modality fusion topics, (automated) synthetic data generation, and complex data sampling -- combined in a single application-oriented benchmark.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 47 of its 79 references that are in the map. First posted on arXiv 21 Feb 2025.

Builds on

47 of 79 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Vanishing Depth: Training Generalized Depth Adapters with Sinusoidal Depth Preprocessing for Pretrained RGB Encoderscited by 0
  2. 2026Visual Quality Score Assessment of Large White Goods in Remanufacture with Multi-View Deformable-DETRcited by 0
  3. 2026A Qualitative Review of GenAI-Based Methods for Data Generation and Augmentation in Industrial Computer Vision Applicationscited by 0