One-shot recognition of any material anywhere using contrastive learning with physics-based rendering (2212.00648v4)

Abstract: Visual recognition of materials and their states is essential for understanding most aspects of the world, from determining whether food is cooked, metal is rusted, or a chemical reaction has occurred. However, current image recognition methods are limited to specific classes and properties and can't handle the vast number of material states in the world. To address this, we present MatSim: the first dataset and benchmark for computer vision-based recognition of similarities and transitions between materials and textures, focusing on identifying any material under any conditions using one or a few examples. The dataset contains synthetic and natural images. The synthetic images were rendered using giant collections of textures, objects, and environments generated by computer graphics artists. We use mixtures and gradual transitions between materials to allow the system to learn cases with smooth transitions between states (like gradually cooked food). We also render images with materials inside transparent containers to support beverage and chemistry lab use cases. We use this dataset to train a siamese net that identifies the same material in different objects, mixtures, and environments. The descriptor generated by this net can be used to identify the states of materials and their subclasses using a single image. We also present the first few-shot material recognition benchmark with images from a wide range of fields, including the state of foods and drinks, types of grounds, and many other use cases. We show that a net trained on the MatSim synthetic dataset outperforms state-of-the-art models like Clip on the benchmark and also achieves good results on other unsupervised material classification tasks.

• The paper presents a novel MatSim dataset and contrastive learning method for effective one-shot recognition of diverse materials.
• The methodology leverages physics-based rendering and an adapted ConvNeXt architecture to capture material similarity under various conditions.
• Evaluation shows the Siamese network outperforms models like CLIP, underscoring its potential in industrial and scientific applications.

One-shot Recognition of Materials: A Comprehensive Overview

The paper, titled "One-shot recognition of any material anywhere using contrastive learning with physics-based rendering," presents a significant advancement in the domain of material recognition using computer vision. This research introduces a novel dataset, MatSim, and an effective approach utilizing contrastive learning and physics-based rendering to achieve one-shot recognition of materials. The approach is aimed at overcoming the limitations of current image recognition methods, which are often constrained to specific classes and properties.

Introduction

Material recognition is a critical task with widespread applications across various fields, including chemistry, construction, and industry. Recognizing material states and transitions, such as determining whether food is cooked or metal is rusted, is essential for automated systems that operate in these domains. However, traditional image recognition methods struggle with the vast variability in material states and settings. The proposed solution, MatSim, is designed to solve this problem by allowing recognition under any conditions using a few examples.

MatSim Dataset

The MatSim dataset is a pivotal component of this research, encapsulating a diverse collection of synthetic and real images. The synthetic images are derived from extensive repositories utilized by CGI artists, providing a vast array of textures, objects, and environmental conditions. This dataset includes gradual transitions and mixtures between materials, effectively simulating continuous changes in states, such as varying cooking levels in food. Furthermore, the dataset supports scenarios common in chemistry labs, such as materials inside transparent vessels.

Methodology

The paper employs a Siamese network trained on the MatSim dataset to capture material similarity across different backgrounds and settings. Through contrastive learning, the network generates descriptors that efficiently identify material states and classes from a single image. This approach emphasizes visual self-similarity, enabling the identification of unfamiliar materials without reliance on predefined categories. Training involved an adaptation of ConvNeXt architecture to handle specific inputs, focusing on region-of-interest (ROI) data for improved material recognition.

Evaluation and Results

The performance of the Siamese network trained on MatSim was rigorously evaluated against well-established benchmarks. The network outperformed state-of-the-art models like CLIP in material recognition tasks, demonstrating robust capabilities across a broad spectrum of material types and conditions. The paper provides empirical evidence of the effectiveness of visually-driven recognition strategies in handling material state identification challenges, outperforming models that rely on semantic labels or human-defined similarity metrics.

Implications and Future Directions

The advances presented in this paper hold practical implications for numerous fields requiring adaptable material recognition systems. By enabling one-shot learning of materials, this approach opens new opportunities for automated systems in manufacturing, quality control, and laboratory settings, among others. The reliance on synthetic datasets for training poses intriguing questions about the potential of simulated environments to generalize real-world conditions, hinting at an intriguing avenue for future research.

The dataset and methodology introduced by this research are likely to stimulate further investigation into leveraging synthetic environments for machine learning, especially where obtaining real-world training data proves challenging or impractical. Future efforts may focus on expanding the dataset, refining the model's architecture, and exploring additional domains where such recognition capabilities can be applied.

In conclusion, this paper provides a comprehensive framework and dataset for material recognition, positioning itself as a foundational work for future research in one-shot learning for various practical applications. Its contributions are invaluable for advancing the technical capabilities of computer vision systems in understanding and interacting with the material world.