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README.md

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----
-license: apache-2.0
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+---
+license: cc-by-nc-sa-4.0
+---
+# Flock4D (tar.gz format)
+
+This dataset contains the Flock4D dataset converted to the VLBM/Flock4D-compatible format. 
+To facilitate easier downloading and storage, the 1000 sequences have been compressed into `.tar.gz` archives in chunks of 50 sequences per archive.
+
+## Dataset Description
+
+- **Source**: Flock4D
+- **Format**: VLBM / Flock4D-compatible per-sequence layout compressed into tar.gz chunks
+- **Contents**: RGB images, dense depth maps, 2D/3D trajectories, camera intrinsics and extrinsics, visibility masks, and scene metadata
+
+### Scale
+
+Flock4D provides 1000 sequences of birds (flocks) flying in various environments.
+There are 24 species of birds included:
+`cannada_goose`, `common_starling`, `cormorant`, `crane`, `crested_bis`, `crow`, `dove`, `duck`, `dunlin`, `eagle`, `egret`, `flamingo`, `jackdaw`, `mallard`, `parrot`, `pelican`, `pigeon`, `red_billed_starling`, `seagull`, `snow_goose`, `stork`, `swallow`, `tit`, `warbler`.
+
+## Dataset Structure
+
+The original dataset is structured by sequence. In this Hugging Face repository, the sequences are grouped and compressed into tarballs (e.g., `flock4d_00000_00049.tar.gz`).
+After extracting a `.tar.gz` archive, each sequence directory follows this layout:
+
+```
+{Species}_{Background}_4k_{ID}/
+├── rgbs/
+│   ├── rgb_00000.jpg
+│   ├── rgb_00001.jpg
+│   └── ...
+├── depths/
+│   ├── depth_00000.npz
+│   ├── depth_00001.npz
+│   └── ...
+├── intrinsics.npy
+├── extrinsics.npy
+├── trajs_2d.npy
+├── trajs_3d.npy
+├── visibilities.npy
+└── scene_info.json
+```
+
+### File Descriptions
+
+- `rgbs/`: RGB frames saved as JPEG (`rgb_XXXXX.jpg`).
+- `depths/`: Dense depth maps saved as compressed NumPy archives (`depth_XXXXX.npz`). Each archive stores a float16 array.
+- `intrinsics.npy`: Camera intrinsic matrices for each frame `(T, 3, 3)`.
+- `extrinsics.npy`: World-to-camera extrinsic matrices (W2C) for each frame `(T, 4, 4)`.
+- `trajs_2d.npy`: 2D trajectories `(T, N, 2)` -- pixel coordinates (x, y).
+- `trajs_3d.npy`: 3D trajectories `(T, N, 3)` -- world-space coordinates (x, y, z); zero-filled where invisible.
+- `visibilities.npy`: Visibility flags `(T, N)` (1.0 visible, 0.0 not visible).
+- `scene_info.json`: JSON file with per-sequence metadata, including camera properties and scene assets.
+
+## Usage Example (Python)
+
+To use the dataset, first download the tarballs and extract them:
+
+```bash
+mkdir -p data/flock4d
+tar -xvf flock4d_00000_00049.tar.gz -C data/flock4d/
+```
+
+Then load the annotations in Python:
+
+```python
+import numpy as np
+from PIL import Image
+from pathlib import Path
+import json
+
+seq_dir = Path("data/flock4d/cannada_goose_abandoned_parking_4k_313")
+
+# Load annotations
+trajs_2d    = np.load(seq_dir / "trajs_2d.npy")     # (T, N, 2)
+trajs_3d    = np.load(seq_dir / "trajs_3d.npy")     # (T, N, 3)
+vis         = np.load(seq_dir / "visibilities.npy") # (T, N)
+intrinsics  = np.load(seq_dir / "intrinsics.npy")   # (T, 3, 3)
+extrinsics  = np.load(seq_dir / "extrinsics.npy")   # (T, 4, 4)
+
+# Load context
+frame_idx = 0
+rgb = Image.open(seq_dir / "rgbs" / f"rgb_{frame_idx:05d}.jpg")
+depth_npz = np.load(seq_dir / "depths" / f"depth_{frame_idx:05d}.npz")
+depth = depth_npz['depth']  # float16 array (H, W)
+
+# Load scene info
+with open(seq_dir / "scene_info.json", 'r') as f:
+    scene_info = json.load(f)
+
+print(scene_info)
+```