Add dataset card

README.md

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+---
+task_categories:
+- depth-estimation
+tags:
+- robotics
+- 3d-reconstruction
+- realsense
+- multi-view
+- stereo
+- tabletop
+size_categories:
+- 1K<n<10K
+---
+
+# RealSense Multi-Camera Tabletop
+
+Synchronized multi-view RGB + stereo IR captures of a tabletop scene from **4 Intel RealSense
+cameras**, recorded with fixed camera positions. Includes FoundationStereo depth for two scenes
+and chessboard-derived extrinsics for merging the views into a single point cloud.
+
+## Layout
+
+```
+scene_000NN/
+├── camera_poses.json          # extrinsics, only in calibration scenes (see table)
+└── <camera_serial>/
+    ├── rgb/00000.jpg ... 00119.jpg      # 640x480 RGB
+    ├── left/00000.jpg  ... 00119.jpg    # 640x480 grayscale, left IR
+    ├── right/00000.jpg ... 00119.jpg    # 640x480 grayscale, right IR
+    ├── stereo_depth/00000.npy           # (480,640) float64, metres — FoundationStereo output
+    ├── stereo_aligned_depth/00000.npy   # (480,640) uint16 — above, reprojected to the colour frame
+    └── meta_info.json                   # per-camera intrinsics + depth_scale
+```
+
+Camera serials, identical across all scenes:
+`234322305266`, `336222300744`, `336522303601`, `339522301222`
+
+## Contents
+
+Every scene has all 4 cameras × 120 frames of `rgb`, `left`, and `right`. Depth and poses are
+**not** uniform across scenes:
+
+| scene | rgb/left/right | stereo_depth | stereo_aligned_depth | camera_poses.json | role |
+|---|---|---|---|---|---|
+| `scene_00001` | 120 | 120 | 120 | — | object |
+| `scene_00002` | 120 | — | — | ✅ | chessboard calibration |
+| `scene_00003` | 120 | — | — | — | object |
+| `scene_00004` | 120 | — | — | — | object |
+| `scene_00005` | 120 | 120 | 120 | — | object |
+| `scene_00006` | 120 | — | — | ✅ | chessboard calibration |
+
+Depth is a derived artifact: it is regenerated by running FoundationStereo on `left/` + `right/`,
+so the four scenes without it can be filled in locally. Calibration scenes only ever need `rgb/`.
+
+## Conventions
+
+**`meta_info.json`** — `depth_intrinsics` and `color_intrinsics` each carry a 9-element
+`intrinsic_matrix` in **column-major** order, i.e. `[fx, 0, 0, 0, fy, 0, cx, cy, 1]`. To use it:
+
+```python
+import json, numpy as np
+m = json.load(open("scene_00001/234322305266/meta_info.json"))
+K = np.array(m["color_intrinsics"]["intrinsic_matrix"]).reshape(3, 3).T
+depth_scale = m["depth_scale"]   # 0.001 -> uint16 units are millimetres
+```
+
+**Depth units** — `stereo_depth/*.npy` is `float64` **metres**. `stereo_aligned_depth/*.npy` is
+`uint16`; multiply by `depth_scale` (0.001) to get metres. Zero means no return.
+
+**`camera_poses.json`** — maps each camera serial to `w2c` and `c2w`, both 4×4 row-major
+homogeneous matrices, as produced by OpenCV chessboard pose estimation. `c2w` is the
+inverse of `w2c`:
+
+```python
+poses = json.load(open("scene_00002/camera_poses.json"))
+c2w = np.array(poses["234322305266"]["c2w"])   # camera -> world
+```
+
+## Merging views
+
+Poses come from a chessboard scene; geometry and colour come from an object scene. This is valid
+**only because the cameras never moved between them** — pair an object scene with a calibration
+scene captured in the same session, matching cameras by serial:
+
+```
+object scene_00001 + poses from scene_00002
+```
+
+Reproject each camera's `stereo_aligned_depth` with its `color_intrinsics`, transform by that
+camera's `c2w`, and concatenate to get a single merged cloud.
+
+## Provenance
+
+Captured and processed with the `realsense_multicam_tabletop` pipeline (RealSense capture →
+FoundationStereo → depth-to-colour alignment → chessboard pose estimation → merge).