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

2025-02-21

AI Summary

This paper addresses the challenges of multi-view and multimodal data fusion in industrial part recognition—including few-shot/many-shot scenarios, high visual similarity, variable part sizes, and production-line deployment constraints. Methodologically, it introduces, for the first time in industrial recognition, multimodal contextual cues—physical attributes, natural language descriptions, and semantic hierarchies—integrated with calibrated multi-view RGB-D acquisition, cross-modal feature alignment and fusion, synthetic data augmentation, and adaptive sampling. Its core contributions are (i) the construction of MVIP, the first multi-view multimodal benchmark tailored to industrial part recognition, and (ii) an evaluation paradigm emphasizing model transferability and real-world deployability. Experiments demonstrate near-perfect Top-5 accuracy (≈100%) under stringent conditions, with significant improvements in both few-shot learning performance and robustness for highly similar parts.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Multi-modal industrial part recognition
• Transferability of state-of-the-art methods
• Application-oriented benchmark for industrial classifiers

Innovation

• Combines RGBD with object context
• Focuses on industrial transferability
• Integrates modality fusion and data generation