README.md

metadata

license: mit
task_categories:
  - object-detection
  - image-segmentation
  - robotics
dataset_info:
  features:
    - name: scene_id
      dtype: string
    - name: image_id
      dtype: string
    - name: obj_id
      dtype: int64
    - name: pose
      sequence:
        sequence: float64
    - name: camera_intrinsics
      sequence:
        sequence: float64
    - name: depth_scale
      dtype: float64
    - name: bbox
      sequence: int64
    - name: visibility
      dtype: float64
    - name: split
      dtype: string
    - name: rgb
      dtype: image
    - name: depth
      dtype: image
    - name: mask
      dtype: image
    - name: mask_visib
      dtype: image
  splits:
    - name: test
      num_bytes: 12240185177.56
      num_examples: 12247
    - name: train
      num_bytes: 8947085481.56
      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 | Paper

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