---
license: mit
task_categories:
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- image-segmentation
- robotics
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  download_size: 7105758283
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configs:
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  data_files:
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    path: data/test-*
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---

# IndustryShapes

[**Project Page**](https://pose-lab.github.io/IndustryShapes) | [**Paper**](https://huggingface.co/papers/2602.05555)

IndustryShapes is a large-scale RGB-D benchmark dataset of industrial tools and components, designed for both instance-level and novel object 6D pose estimation. It bridges the gap between lab-based research and real-world industrial deployment by providing realistic scenes captured in industrial assembly settings.

### Dataset Features
Unlike traditional datasets focused on household products, IndustryShapes introduces five new object types with challenging properties. The dataset features:
- **Realistic Settings:** Objects captured in authentic industrial assembly environments.
- **Diverse Complexity:** Scenes ranging from simple to challenging, including single and multiple objects, as well as multiple instances of the same object.
- **Unique Modalities:** It is the first dataset to offer RGB-D static onboarding sequences to support model-free and sequence-based approaches.
- **Comprehensive Annotations:** Includes high-quality annotated poses, bounding boxes, and segmentation masks.

### Dataset Organization
The dataset is organized into two parts:
- **Classic Set:** Includes a total of 4.6k images and 6k annotated poses.
- **Extended Set:** Introduces additional data modalities for advanced evaluation of model-free and sequence-based methods.

### Supported Tasks
- **6D Object Pose Estimation** (Instance-level and Novel Object)
- **Object Detection**
- **Image Segmentation**
- **Robotics**