| --- |
| license: cc-by-nc-4.0 |
| pretty_name: "MOO: Multi-view Oriented Observations" |
| task_categories: |
| - image-feature-extraction |
| tags: |
| - re-identification |
| - cattle |
| - animals |
| - synthetic |
| - multi-view |
| - depth |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: topside |
| default: true |
| data_files: |
| - split: train |
| path: [ data/train_top-*, data/train_side-* ] |
| - split: query |
| path: [ data/test_top_query-*, data/test_side_query-* ] |
| - split: gallery |
| path: [ data/test_top_gallery-*, data/test_side_gallery-* ] |
| - config_name: top |
| data_files: |
| - split: train |
| path: data/train_top-* |
| - split: query |
| path: data/test_top_query-* |
| - split: gallery |
| path: data/test_top_gallery-* |
| - config_name: side |
| data_files: |
| - split: train |
| path: data/train_side-* |
| - split: query |
| path: data/test_side_query-* |
| - split: gallery |
| path: data/test_side_gallery-* |
| --- |
| |
| # MOO: Multi-view Oriented Observations |
|
|
| [Paper (arXiv:2603.04314)](https://arxiv.org/abs/2603.04314) · [Code](https://github.com/TurtleSmoke/MOO) · [Project page](https://turtlesmoke.dev/MOO/) |
|
|
| Presented at the CVPR 2026 Workshop on Computer Vision for Animal Behavior Tracking and Modeling (CV4Animals), |
| non-archival. |
|
|
| MOO provides 128,000 images of 1,000 synthetic cattle identities rendered from 128 calibrated viewpoints, |
| spanning 360° in azimuth and -25° to 90° in elevation, with depth maps and camera parameters for every image. |
|
|
| ## Protocol |
|
|
| Identities are split 500/500, with even IDs for training and odd IDs for testing. |
| Views `0-63` are side views (elevation < 30°), views `64-127` are top views (elevation > 30°). |
| For each test identity, half of its top views and half of its side views are queries, the rest are gallery. |
| The split definition lives in [`make_splits.py`](https://github.com/TurtleSmoke/MOO/blob/main/make_splits.py). |
|
|
| Each config (`top`, `side`, `topside`) has `train`, `query` and `gallery` splits. The paper's evaluation protocols |
| (query → gallery) combine them: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| train = load_dataset("CEAai/MOO", "topside", split="train") |
| |
| # Top -> Side |
| query = load_dataset("CEAai/MOO", "top", split="query") |
| gallery = load_dataset("CEAai/MOO", "side", split="gallery") |
| # Side -> Top uses ("side", "query") vs ("top", "gallery"), Top-Side -> Top-Side uses ("topside", ...) for both |
| ``` |
|
|
| The default download includes depth (~35% of the data). To skip it, stream and select columns. Only the |
| requested columns are fetched (without `streaming=True`, whole files are downloaded and the columns dropped afterwards): |
|
|
| ```python |
| query = load_dataset("CEAai/MOO", "top", split="query", streaming=True, columns=["image", "id", "view_id"]) |
| ``` |
|
|
| Use `.with_format("numpy")` to get `depth` as a float32 array. |
|
|
| ## Fields |
|
|
| | Field | Type | Description | |
| |------------------------|-----------------|---------------------------------------------------------------| |
| | `image` | image | 512×512 RGB render | |
| | `depth` | float32 512×512 | depth map, the background is `1e10` and can be used as a mask | |
| | `id` | int32 | identity, `0-999` | |
| | `view_id` | int32 | viewpoint index in the 128-point grid | |
| | `view` | string | coarse direction label (`front`, `back-left`, `top-right`, …) | |
| | `azimuth`, `elevation` | float64 | camera angles in degrees | |
| | `location` | float64[3] | camera position in world coordinates | |
| | `cam2world` | float64 4×4 | camera-to-world matrix | |
| | `K` | float64 3×3 | camera intrinsics | |
|
|
| ## License |
|
|
| Copyright (c) 2026 Commissariat à l'énergie atomique et aux énergies alternatives (CEA). |
| MOO is released under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{grolleau2026moo, |
| title = {MOO: A Multi-view Oriented Observations Dataset for Viewpoint Analysis in Cattle Re-Identification}, |
| author = {Grolleau, William and Chaouch, Achraf and Sabourin, Astrid and Lapouge, Guillaume and Achard, Catherine}, |
| journal = {arXiv preprint arXiv:2603.04314}, |
| note = {Presented at the CVPR 2026 Workshop on Computer Vision for Animal Behavior Tracking and Modeling (CV4Animals), non-archival}, |
| year = {2026}, |
| doi = {10.48550/arXiv.2603.04314} |
| } |
| ``` |
|
|
