Update README.md

README.md

@@ -1,127 +1,123 @@
 ---
-dataset_info:
-- config_name: default
-  features:
-  - name: idx
-    dtype: int32
-  - name: img
-    dtype: image
-  - name: width
-    dtype: int32
-  - name: height
-    dtype: int32
-  - name: category
-    dtype:
-      class_label:
-        names:
-          '0': bed
-          '1': bookcase
-          '2': chair
-          '3': desk
-          '4': misc
-          '5': sofa
-          '6': table
-          '7': tool
-          '8': wardrobe
-  - name: mask
-    dtype: image
-  - name: model_id
-    dtype: string
-  - name: img_source
-    dtype:
-      class_label:
-        names:
-          '0': ikea
-          '1': internet
-          '2': self-taken
-  - name: keypoints_2d
-    list:
-      list:
-        list: float32
-  - name: bbox
-    list: int32
-  - name: rot_mat
-    list:
-      list: float32
-  - name: trans_mat
-    list: float32
-  - name: cam_position
-    list: float32
-  - name: focal_length
-    dtype: float32
-  - name: inplane_rotation
-    dtype: float32
-  - name: truncated
-    dtype: bool
-  - name: occluded
-    dtype: bool
-  - name: slightly_occluded
-    dtype: bool
-  splits:
-  - name: train
-    num_bytes: 1469522530
-    num_examples: 10069
-  download_size: 3478665604
-  dataset_size: 1469522530
-- config_name: models
-  features:
-  - name: model_id
-    dtype: string
-  - name: category
-    dtype:
-      class_label:
-        names:
-          '0': bed
-          '1': bookcase
-          '2': chair
-          '3': desk
-          '4': misc
-          '5': sofa
-          '6': table
-          '7': tool
-          '8': wardrobe
-  - name: model
-    struct:
-    - name: bytes
-      dtype: binary
-    - name: path
-      dtype: string
-  - name: model_raw
-    struct:
-    - name: bytes
-      dtype: binary
-    - name: path
-      dtype: string
-  - name: model_source
-    dtype:
-      class_label:
-        names:
-          '0': ikea
-          '1': self-scanned
-  - name: voxel_model
-    dtype:
-      array3_d:
-        shape:
-        - 128
-        - 128
-        - 128
-        dtype: uint8
-  - name: keypoints_3d
-    list:
-      list: float32
-  splits:
-  - name: train
-    num_bytes: 2261052746
-    num_examples: 735
-  download_size: 6909052692
-  dataset_size: 2261052746
+license: cc-by-4.0
+task_categories:
+- image-to-3d
+- image-segmentation
+- keypoint-detection
+tags:
+- 3d
+- voxel
+- mesh
+- pose-estimation
+- computer-vision
+pretty_name: Pix3D
+size_categories:
+- 10K<n<100K
 configs:
 - config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+  data_files: data/default-*
 - config_name: models
-  data_files:
-  - split: train
-    path: models/train-*
+  data_files: data/models-*
 ---
+
+# Pix3D
+
+Pix3D is a large-scale dataset of real images paired with pixel-aligned 3D shapes, released alongside
+[Sun, Wu, Zhang et al., *Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling* (CVPR 2018)](http://pix3d.csail.mit.edu/papers/pix3d_cvpr.pdf).
+This Hub repo repackages the [official release](https://github.com/xingyuansun/pix3d) into two Hugging Face
+`datasets` configs so both the image annotations and the underlying 3D assets can be loaded directly with
+`load_dataset`, with no manual download/unzip step.
+
+- **Project page:** http://pix3d.csail.mit.edu
+- **Original repository:** https://github.com/xingyuansun/pix3d
+- **License:** [Creative Commons Attribution 4.0 International (CC BY 4.0)](http://creativecommons.org/licenses/by/4.0/)
+
+## Dataset structure
+
+The dataset is split into two configs because 10,069 image annotations reference only 735 unique physical
+3D objects (many objects were photographed multiple times). Keeping them separate avoids duplicating each
+mesh/voxel grid across every image that uses it; join the two configs on `model_id` when you need both.
+
+```python
+from datasets import load_dataset
+
+annotations = load_dataset("unstructured/pix3d", "default")   # 10,069 rows, one per image
+models = load_dataset("unstructured/pix3d", "models")          # 735 rows, one per unique 3D object
+```
+
+### `default` config — one row per annotated image
+
+| Column | Type | Description |
+|---|---|---|
+| `idx` | int32 | Unique incrementing row identifier |
+| `img` | Image | The photograph containing the object |
+| `width`, `height` | int32 | Image dimensions (`img_size` in the original JSON) |
+| `category` | ClassLabel | Object category (`bed`, `bookcase`, `chair`, `desk`, `misc`, `sofa`, `table`, `tool`, `wardrobe`) |
+| `mask` | Image | Binary segmentation mask isolating the object in `img` |
+| `model_id` | string | Join key into the `models` config identifying which 3D object appears in this image |
+| `img_source` | ClassLabel | Image provenance: `ikea`, `internet`, or `self-taken` |
+| `keypoints_2d` | list[list[list[float]]] | 2D keypoints per annotator, shape `[n_annotators, n_keypoints, 2]`, in pixel coordinates (origin top-left, `+x` right, `+y` down); `[-1, -1]` marks a keypoint an annotator judged unlabelable (e.g. occluded) |
+| `bbox` | list[int] | Object bounding box `[x_from, y_from, x_to, y_to]` |
+| `rot_mat`, `trans_mat` | list[float] | Rotation matrix (3x3) and translation vector applied to the object for rendering |
+| `cam_position`, `inplane_rotation` | list[float] / float | Estimated camera position and in-plane rotation (object-centered), used to evaluate viewer-centered predictions against the canonical view |
+| `focal_length` | float | Estimated focal length in mm (sensor width fixed at 32mm) |
+| `truncated`, `occluded`, `slightly_occluded` | bool | Visibility flags for the object in the image (`occluded` and `slightly_occluded` are mutually exclusive) |
+
+### `models` config — one row per unique 3D object
+
+| Column | Type | Description |
+|---|---|---|
+| `model_id` | string | Unique identifier for this 3D object; matches `model_id` in the `default` config |
+| `category` | ClassLabel | Same category enumeration as the `default` config |
+| `model` | binary (glb) | The object's textured mesh, converted from the original `.obj`/`.mtl`/texture files into a single self-contained binary glTF (`.glb`) blob via `trimesh`, analogous to how `img`/`mask` embed PNG bytes |
+| `model_raw` | binary (glb) or null | Raw, unprocessed scanned mesh, same glb encoding as `model`. Only populated for objects from the full Pix3D release that includes raw scans (`self-scanned` sources); this standard release does not include raw scans, so this column is `null` for every row here |
+| `model_source` | ClassLabel | 3D model provenance: `ikea` or `self-scanned` |
+| `voxel_model` | Array3D (128×128×128, uint8) | Voxelized occupancy grid of the object, parsed from the original `voxel.mat` file (`1` = occupied) |
+| `keypoints_3d` | list[list[float]] | 3D keypoint positions `[n_keypoints, 3]` in the model's canonical coordinate frame (`+x` left, `+y` up, `+z` inward) |
+
+### Coordinate conventions (from the original release)
+
+- **2D keypoints / image pixels:** origin top-left, `+x` right, `+y` down.
+- **Mesh (`model`) canonical frame:** `+x` left, `+y` up, `+z` inward.
+- **Voxel (`voxel_model`) canonical frame:** `+x` left, `+y` outward, `+z` up.
+- `rot_mat`/`trans_mat` are defined in camera coordinates and applied to the object for rendering; they are
+  not directly usable to rotate viewer-centered predictions back to canonical view. Use `cam_position` and
+  `inplane_rotation` instead — both are defined in the object's own coordinate frame and reproduce the
+  original image up to a homography, which is what the original authors used for viewpoint evaluation.
+
+## Category / label distribution
+
+| Category | # images |
+|---|---|
+| chair | 3,839 |
+| sofa | 1,947 |
+| table | 1,870 |
+| bed | 994 |
+| desk | 700 |
+| bookcase | 361 |
+| wardrobe | 243 |
+| misc | 68 |
+| tool | 47 |
+
+## License and attribution
+
+The Pix3D dataset is licensed under a
+[Creative Commons Attribution 4.0 International License (CC BY 4.0)](http://creativecommons.org/licenses/by/4.0/).
+You are free to share and adapt the data for any purpose, including commercially, provided you give
+appropriate credit to the original authors.
+
+If you use this dataset, please cite the original paper:
+
+```bibtex
+@inproceedings{pix3d,
+  title={Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling},
+  author={Sun, Xingyuan and Wu, Jiajun and Zhang, Xiuming and Zhang, Zhoutong and Zhang, Chengkai and Xue, Tianfan and Tenenbaum, Joshua B and Freeman, William T},
+  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2018}
+}
+```
+
+For questions about the original dataset, contact Xingyuan Sun (xingyuansun.cs@gmail.com) or Jiajun Wu
+(jiajunwu@mit.edu). This Hub repackaging was produced by converting the official release's `pix3d.json`
+and asset folders (`img/`, `mask/`, `model/`) into the two configs described above; no annotation values
+were modified, only the file layout and encoding (mesh → glb, voxel `.mat` → numpy array).