| --- |
| license: cc-by-nc-4.0 |
| pretty_name: imagenet3d |
| --- |
| ## ImageNet3D |
|
|
| We present **ImageNet3D**, a large dataset for general-purpose object-level 3D understanding. ImageNet3D augments 200 categories from the ImageNet dataset with 2D bounding box, 3D pose, 3D location annotations, and image captions interleaved with 3D information. |
|
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| Refer to [github.com/wufeim/imagenet3d](https://github.com/wufeim/imagenet3d) for the full documentation and sample preprocessing code for ImageNet3D. |
|
|
| ### Download Data |
|
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| **ImageNet3D-v1.0:** Directly download from the HuggingFace WebUI, or on a server, run |
|
|
| ```sh |
| wget https://huggingface.co/datasets/ccvl/ImageNet3D/resolve/main/imagenet3d_v1.zip |
| ``` |
|
|
| **Future updates:** We are working on annotating more object categories and improving the quality of current annotations. |
| The next update is planned to be released by the end of Jan 2025. Please let us know if you have any suggestions for future updates. |
|
|
| ### Example Usage |
|
|
| ```py |
| from PIL import Image |
| import numpy as np |
| |
| img_path = 'imagenet3d/bed/n02818832_13.JPEG' |
| annot_path = 'imagenet3d/bed/n02818832_13.npz' |
| |
| img = np.array(Image.open(img_path).convert('RGB')) |
| annot = dict(np.load(annot_path, allow_pickle=True))['annotations'] |
| |
| # Number of objects |
| num_objects = len(annot) |
| |
| # Annotation of the first object |
| azimuth = annot[0]['azimuth'] # float, [0, 2*pi] |
| elevation = annot[0]['elevation'] # float, [0, 2*pi] |
| theta = annot[0]['theta'] # float, [0, 2*pi] |
| cad_index = annot[0]['cad_index'] # int |
| distance = annot[0]['distance'] # float |
| viewport = annot[0]['viewport'] # int |
| img_height = annot[0]['height'] # numpy.uint16 |
| img_width = annot[0]['width'] # numpy.uint16 |
| bbox = annot[0]['bbox'] # numpy.ndarray, (x1, y1, x2, y2) |
| category = annot[0]['class'] # str |
| principal_x = annot[0]['px'] # float |
| principal_y = annot[0]['py'] # float |
| |
| # label indicating the quality of the object, occluded or low quality |
| object_status = annot[0]['object_status'] # str, one of ('status_good', 'status_partially', 'status_barely', 'status_bad') |
| |
| # label indicating if multiple objects from same category very close to each other |
| dense = annot[0]['dense'] # str, one of ('dense_yes', 'dense_no') |
| ``` |
|
|
