Datasets:
| 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://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** |