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tartanair-fish-v3-parquet
Per-frame Parquet dataset for ReCAST tracker training.
Schema
One row per frame, grouped by episode_id. Arrow memory-mapped access
enables reading specific frames without loading entire episodes.
| Column | Type | Description |
|---|---|---|
| episode_id | int32 | Episode identifier |
| frame_idx | int32 | Frame index within episode |
| jpeg | binary | JPEG-encoded RGB frame |
| depth | list<float32> | Flat H×W depth map |
| seg | list<uint16> | Semantic segmentation (empty if absent) |
| pose | fixed_size_list<float64, 16> | 4×4 camera-to-world pose (vision convention) |
| intrinsics | fixed_size_list<float32, 4> | [fx, fy, cx, cy] in pixels |
| gravity | fixed_size_list<float32, 3> | World-frame gravity direction |
| camera_model | string | "pinhole" or "equidistant" |
Conventions
- Poses: Vision convention (X=right, Y=down, Z=forward). Normalized during conversion.
- Depth: Projective z-depth (all datasets, normalized during conversion).
- Gravity: Unit vector pointing downward in world frame.
Provenance
- Dataset: tartanair
- Episodes: 0
- Frames: 0
- Created: 2026-03-15T19:46:29.610714+00:00
- Build script:
scripts/convert_episodes_to_parquet.py - Git commit:
652912ed324118c3d69a652be186f0ca129de190
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