Datasets:
File size: 2,512 Bytes
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---
license: mit
task_categories:
- object-detection
- image-segmentation
- robotics
dataset_info:
features:
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dtype: string
- name: image_id
dtype: string
- name: obj_id
dtype: int64
- name: pose
sequence:
sequence: float64
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sequence:
sequence: float64
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sequence: int64
- name: visibility
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- name: depth
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splits:
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num_bytes: 12240185177.56
num_examples: 12247
- name: train
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num_examples: 10222
download_size: 7105758283
dataset_size: 21187270659.12
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
---
# 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** |