Datasets:
File size: 3,680 Bytes
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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).
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