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--- |
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license: mit |
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task_categories: |
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- object-detection |
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- image-segmentation |
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- robotics |
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dataset_info: |
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features: |
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- name: scene_id |
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dtype: string |
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- name: image_id |
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dtype: string |
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- name: obj_id |
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dtype: int64 |
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- name: pose |
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sequence: |
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sequence: float64 |
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- name: camera_intrinsics |
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sequence: |
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sequence: float64 |
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- name: depth_scale |
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dtype: float64 |
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- name: bbox |
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sequence: int64 |
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- name: visibility |
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dtype: float64 |
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- name: split |
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dtype: string |
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- name: rgb |
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dtype: image |
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- name: depth |
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dtype: image |
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- name: mask |
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dtype: image |
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- name: mask_visib |
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dtype: image |
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splits: |
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- name: test |
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num_bytes: 12240185177.56 |
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num_examples: 12247 |
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- name: train |
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num_bytes: 8947085481.56 |
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num_examples: 10222 |
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download_size: 7105758283 |
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dataset_size: 21187270659.12 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/test-* |
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- split: train |
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path: data/train-* |
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--- |
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# IndustryShapes |
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[**Project Page**](https://pose-lab.github.io/IndustryShapes) | [**Paper**](https://arxiv.org/abs/2602.05555) |
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IndustryShapes is a new benchmark dataset tailored for 6D object pose estimation in industrial settings. Targeting the challenges of textureless objects, reflective surfaces, and complex assembly tools, this dataset provides high-quality RGB-D data with precise annotations to advance the state of the art in robotic manipulation. |
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### Dataset Features |
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Unlike traditional datasets focused on household products, IndustryShapes introduces five new industry-relevant object types with challenging properties. The dataset features: |
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- **Realistic Settings:** Objects captured in authentic industrial assembly environments. |
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- **Diverse Complexity:** Scenes ranging from simple to challenging, including single and multiple objects, as well as multiple instances of the same object. |
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- **Unique Modalities:** It is the first dataset to offer RGB-D static onboarding sequences to support model-free and sequence-based approaches. |
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- **Comprehensive Annotations:** Includes high-quality annotated poses, bounding boxes, and segmentation masks. |
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### Dataset Organization |
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The dataset is organized into two parts: |
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- **Classic Set:** The Classic Set supports instance-level pose estimation with 21 scenes (13 train, 8 test). Includes images from real industrial scenes with varying complexity, Lab captured and Synthetically generated data. |
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- **Extended Set:** Inlucdes three challenging office scenes with unconstrained lighting, distractors, occlusions and diverse viewpoints featuring all objects. It also includes 10 **RGB-D** static onboarding sequences (2 per object). |
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### Tasks |
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- **6D Object Pose Estimation** (Instance-level and Novel Object) |
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- **Object Detection** |
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- **Image Segmentation** |
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- **Robotic Manipulation** |
