| --- |
| 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**](https://pose-lab.github.io/IndustryShapes) | [**Paper**](https://arxiv.org/abs/2602.05555) |
|
|
| 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. |
|
|
| ### Dataset Features |
| Unlike traditional datasets focused on household products, IndustryShapes introduces five new industry-relevant 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:** 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. |
| - **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). |
|
|
| ### Tasks |
| - **6D Object Pose Estimation** (Instance-level and Novel Object) |
| - **Object Detection** |
| - **Image Segmentation** |
| - **Robotic Manipulation** |
