---
license: cc-by-nc-4.0
pretty_name: "SpineDepth per-frame: raw RGB-D frames, CT meshes, per-frame poses and a reconstruction script"
tags:
- medical-imaging
- rgbd
- depth-camera
- computed-tomography
- spine
- point-cloud
- registration
- pose-estimation
- orthopedics
size_categories:
- 1K<n<10K
---

# SpineDepth per-frame: raw RGB-D frames, CT meshes and per-frame ground-truth poses

This is a frame-wise release of the SpineDepth dataset (Liebmann et al., 2021) for CT to
intraoperative RGB-D registration and vertebra pose estimation. It contains the raw per-frame
data: RGB-D point clouds with a vertebra label on every point, the CT bone meshes, and the
optical-tracking pose of every vertebra in every frame. A small script turns the raw data into
per-vertebra point clouds in camera space and in CT space.

## How this differs from SpineDepth_segmented

Our earlier release,
[`luohwu/SpineDepth_segmented`](https://huggingface.co/datasets/luohwu/SpineDepth_segmented),
contains merged point clouds. All tracked frames of a camera were fused into one downsampled
CT-space cloud per vertebra, so each vertebra has two clouds, one per camera. It is a static
registration set with no per-frame information.

This release keeps every RGB-D frame as its own point cloud. The frames are consecutive frames
of the original 30 Hz recordings. Each frame comes with the full raw scene in camera space, a
vertebra label per point, and the pose of every vertebra in that frame. Nothing is fused, filtered
or downsampled. Use SpineDepth_segmented for clean CT to RGB-D surface registration. Use this
dataset when you need single depth frames, short temporal sequences, per-frame poses, the
surrounding scene, or the raw sensor noise.

Fully reconstructed per-frame files are large, so we ship the compact raw data together with the
reconstruction script instead of prebuilt point clouds. Reconstructing one specimen takes a few
minutes on a desktop machine.

## Quick start

You need Python 3.8 or newer with numpy and open3d 0.15 or newer. The script finds `raw/` and
writes `reconstructed/` relative to its own location, so the folder can be unpacked anywhere and
no paths need editing. Run it from any working directory.

```bash
pip install -r requirements.txt
python reconstruct_frames.py                  # reconstructs specimen 2 (default) into ./reconstructed
python reconstruct_frames.py --specimen 7     # one specimen (1..10)
python reconstruct_frames.py --specimen all   # all ten specimens (~35 GB, ~20 min with 12 workers)
python reconstruct_frames.py --specimen 3 --workers 8 --out-root /some/other/folder
```

## Dataset summary

- 10 cadaveric specimens (IDs `1` to `10`), lumbar levels L1 to L5.
- 50 CT bone meshes (`.stl`), one per specimen and level.
- 2 RGB-D cameras (Stereolabs ZED Mini, 1920×1080, 30 Hz) per recording, 40 recordings per
  specimen (10 pedicle screws × 4 surgical steps).
- Per frame: an RGB-D point cloud in camera space with a vertebra label on every point, and the
  pose of each of the five vertebrae (CT to camera).
- Frames per recording and camera: 4 consecutive frames (`frame_0` to `frame_3`, the first four
  frames of each video). A few recording and camera pairs have fewer or no frames; see the
  per-specimen overview below. The full videos, several hundred frames per recording, are
  available as SVO files from the original SpineDepth release.

## Directory structure

```
SpineDepth_perframe/
├── README.md
├── requirements.txt
├── reconstruct_frames.py                      # builds ./reconstructed from ./raw
├── raw/                                       # shipped
│   └── Specimen_<s>/                          # s ∈ {1..10}
│       ├── STL/
│       │   └── L1.stl  L2.stl  L3.stl  L4.stl  L5.stl       # CT bone meshes, each in its own CT space
│       ├── transformation_matrices.txt                        # unused by the script, see "Raw file formats"
│       └── recordings/
│           └── recording<r>/                  # r ∈ {0..39}
│               ├── Poses_0.txt                # ground-truth poses for camera 0, every video frame
│               ├── Poses_1.txt                # ground-truth poses for camera 1, every video frame
│               └── Specimen_<s>_recording_<r>_cam_<c>_frame_<f>.npz   # c ∈ {0,1}, f ∈ {0..3}
└── reconstructed/                             # created by reconstruct_frames.py (not shipped)
    ├── index.csv                              # one row per (specimen, camera, recording, frame, level)
    └── Specimen_<s>/
        ├── index.csv
        ├── CT/L1.stl … L5.stl                 # copied from raw
        ├── camera_0/
        │   └── recording_<rr>/                # rr ∈ {00..39}
        │       └── frame_<f>/
        │           ├── frame_camera_space.ply          # full raw RGB-D frame, camera space, RGB
        │           ├── frame_camera_space_labels.npy   # int8 label per point of the file above
        │           ├── poses_ct_to_camera.txt          # 5 rows (L1..L5), p_camera = T @ p_ct
        │           ├── L1_camera_space.ply  …  L5_camera_space.ply   # labelled points, camera space, RGB
        │           └── L1_ct_space.ply      …  L5_ct_space.ply       # same points in the CT space of that level
        └── camera_1/
            └── (identical layout)
```

## Raw file formats (`raw/`)

| File | Format | Content |
|---|---|---|
| `Specimen_<s>_recording_<r>_cam_<c>_frame_<f>.npz` | NumPy `.npz`, key `arr_0`, float64 array of shape `(N, 7)` | Columns `x, y, z` (millimetres, camera space), `r, g, b` (in `[0, 1]`), `label` (`0` background, `1` to `5` = L1 to L5). One file per video frame `f` of camera `c`. |
| `Poses_<c>.txt` | text, 16 comma-separated floats per line | Poses for every video frame of camera `c`, 5 lines per frame in the order L1..L5. Line `5*f + (k-1)` is the 4×4 matrix `T` of vertebra L*k* at frame `f`, flattened row-major, with `p_camera = T @ p_ct`. The file covers the whole video, so it has many more frames than there are npz files. |
| `STL/L<k>.stl` | binary STL | CT-segmented bone surface of vertebra L*k*, in its own CT space. |
| `transformation_matrices.txt` | text, 4 lines × 16 floats | Four 4×4 matrices carried over verbatim from the original preprocessing. The reconstruction script does not use them and their meaning is not documented here. |

## Reconstructed file formats (`reconstructed/`)

| File | Format | Notes |
|---|---|---|
| `frame_camera_space.ply` | binary PLY, `double x y z`, `uchar red green blue` | Every point of the npz frame: bone, soft tissue, instruments, hands, background. Camera space. |
| `frame_camera_space_labels.npy` | NumPy `int8`, shape `(N,)` | Same point order as the ply above. `0` = background, `1` to `5` = L1 to L5. |
| `poses_ct_to_camera.txt` | 5 lines × 16 comma-separated floats | The five pose rows of this frame, copied from `Poses_<c>.txt`. Line *k* = vertebra L*k*. |
| `L<k>_camera_space.ply` | binary PLY, as above | Points of the frame whose label equals *k*. Camera space. |
| `L<k>_ct_space.ply` | binary PLY, as above | `L<k>_camera_space.ply` transformed by `inv(T_k)`. Overlays directly on `CT/L<k>.stl`. |
| `CT/L<k>.stl` | binary STL | Copy of the raw mesh, stored once per specimen. |
| `index.csv` | CSV | Columns: `specimen, camera, recording, frame, level, num_points_level, num_points_frame, camera_space_ply, ct_space_ply, ct_mesh`. Paths relative to `reconstructed/`. |

## Coordinate spaces

| Space | Definition | Files |
|---|---|---|
| camera space | Optical frame of the ZED Mini that captured the frame. Millimetres. Each camera and each recording has its own camera space, because the cameras were repositioned before every pedicle screw. | npz `x y z`, `frame_camera_space.ply`, `L<k>_camera_space.ply` |
| CT space of vertebra L<k> | Coordinate frame of `STL/L<k>.stl`. Millimetres. Each vertebra has its own CT space. The five CT spaces of one specimen are not aligned with each other, because every vertebra was tracked by its own optical marker. | `L<k>.stl`, `L<k>_ct_space.ply` |

For vertebra L<k> in a given frame, `p_camera = T_k @ p_ct`, where `T_k` is the pose line of that
vertebra (4×4 homogeneous). Equivalently `L<k>_ct_space = inv(T_k) @ L<k>_camera_space`.

## Provenance

The data start from the original SpineDepth recordings (stereo RGB-D video tracked by an optical
tracking system) and the CT bone models.

1. Frame export. The first four frames of each recording and camera were exported from the SVO
   videos as RGB-D point clouds in camera space. These are the npz files.
2. Poses. For every video frame and every vertebra, the pose `T_k` (CT to camera) comes from the
   optical tracker. Each vertebra carried its own marker, fixed with K-wires and calibrated to the
   CT in the original SpineDepth work. The poses are stored in `Poses_<c>.txt`.
3. Vertebra labels. The label column was derived from the tracked CT models; no model predicts
   it. The labels behave like a projected silhouette mask: every point along a camera pixel that
   falls inside the rendered outline of vertebra L*k* receives label *k*. So points of
   instruments, hands or soft tissue in front of the bone, and depth outliers, can carry a bone
   label. Typical distances between labelled points and the CT surface are a few millimetres,
   with a tail up to about 2 cm. The script applies no 3D distance filtering, so you can choose
   your own cleaning step, for example keeping only points within 5 mm of the CT surface.
4. Reconstruction. `reconstruct_frames.py` splits each npz by label, writes the per-vertebra
   clouds in camera space, and transforms them by `inv(T_k)` into CT space.

### Recording protocol (from Liebmann et al., 2021)

Each specimen received ten pedicle screws (L1 to L5, both sides). For every screw, four surgical
steps were recorded separately: (1) entry point with a luer, (2) canal preparation with an awl,
(3) canal integrity check with a K-wire, (4) screw placement. Both cameras were repositioned and
recalibrated before every screw, so recordings `4n` to `4n+3` share a camera pose and the camera
pose jumps between such groups. Within a specimen the vertebrae barely move relative to each
other. For specimen 2, the relative poses of adjacent levels vary by less than 0.3 mm and 0.2°
over the whole procedure. The absolute pose drifts by up to a few millimetres within a recording
as the surgeon works on the bone. The per-frame poses account for all of this.

## Per-specimen overview

| Specimen | Exposure (orig. paper) | Screw order (orig. paper) | Recordings with frames | Recording/camera pairs (of 80) | npz frames | Video frames per camera |
|---|---|---|---|---|---|---|
| 1 | Midline approach, soft tissue intact | L1–L5 | 4 | 4 | 16 | 11,278 |
| 2 | Full exposure | L1–L3, L4–L5 | 40 | 80 | 320 | 18,194 |
| 3 | Full exposure | L1–L2, L3–L5 (LLL, RRR) | 40 | 79 | 313 | 16,747 |
| 4 | Full exposure | L1–L5 | 40 | 79 | 307 | 18,746 |
| 5 | Full exposure | L1–L3, L4–L5 | 40 | 80 | 320 | 15,649 |
| 6 | Full exposure | L3–L4, L4–L5, L1–L2 | 40 | 68 | 272 | 15,126 |
| 7 | Full exposure | L4–L5, L1–L3 | 40 | 80 | 320 | 12,871 |
| 8 | Full exposure | L2–L4, L4–L5, L1–L2 | 40 | 80 | 320 | 13,844 |
| 9 | Full exposure | L1–L5 | 40 | 74 | 276 | 12,202 |
| 10 | Full exposure | L4–L5, L1–L3 | 30 | 55 | 220 | 12,825 |
| Total | | | | | 2,684 | 147,482 |

"Recording/camera pairs" counts the recording and camera combinations that have at least one
npz frame; a complete specimen has 40 × 2 = 80. Most pairs have 4 frames; a handful in specimens
3, 4 and 9 have 1 to 3. "Video frames per camera" is the number of frames in the full original
videos (the length of `Poses_<c>.txt` divided by 5). Screw-order groups are separated by commas.
Previously inserted screws were removed before the next group unless they reappear in it.
Specimen 1 was instrumented through a standard midline approach with the soft tissue left intact.
Its bone surface is largely occluded and only four recordings were exported.

Reconstruction output is about 12.5 MB per frame, roughly 4 GB per full specimen and about 34 GB
for all ten.

## Usage

```python
import numpy as np, open3d as o3d

root  = "reconstructed/Specimen_2"
frame = f"{root}/camera_0/recording_00/frame_0"

# CT <-> RGB-D registration of L3 (already in a common frame):
ct    = o3d.io.read_triangle_mesh(f"{root}/CT/L3.stl")
rgbd  = o3d.io.read_point_cloud(f"{frame}/L3_ct_space.ply")

# Pose estimation: predict T from camera-space points, compare against ground truth
pts   = o3d.io.read_point_cloud(f"{frame}/L3_camera_space.ply")
T_gt  = np.loadtxt(f"{frame}/poses_ct_to_camera.txt", delimiter=",").reshape(5, 4, 4)[2]  # L3

# Place the CT model into the camera-space scene
ct_in_camera = o3d.io.read_triangle_mesh(f"{root}/CT/L3.stl").transform(T_gt)
scene  = o3d.io.read_point_cloud(f"{frame}/frame_camera_space.ply")
labels = np.load(f"{frame}/frame_camera_space_labels.npy")

# Or work directly on the raw npz without reconstructing:
raw = np.load("raw/Specimen_2/recordings/recording0/Specimen_2_recording_0_cam_0_frame_0.npz")["arr_0"]
xyz, rgb, label = raw[:, :3], raw[:, 3:6], raw[:, 6].astype(int)
poses = np.loadtxt("raw/Specimen_2/recordings/recording0/Poses_0.txt", delimiter=",").reshape(-1, 5, 4, 4)
T_L3_frame0 = poses[0, 2]
```

## License

Released under CC BY-NC 4.0, following the original SpineDepth dataset.

## Citation

If you use this dataset, please cite our work and the original SpineDepth paper:

```bibtex
@article{wu2026neuralbonereg,
  title   = {NeuralBoneReg: An instance-specific label-free point cloud-based method for multi-modal bone surface registration},
  author  = {Wu, Luohong and Seibold, Matthias and Cavalcanti, Nicola A. and Ao, Yunke and Flepp, Roman and Massalimova, Aidana and Calvet, Lilian and F{\"u}rnstahl, Philipp},
  journal = {Medical Image Analysis},
  year    = {2026},
  doi     = {10.1016/j.media.2026.104133}
}

@article{liebmann2021spinedepth,
  title   = {{SpineDepth}: A Multi-Modal Data Collection Approach for Automatic Labelling and Intraoperative Spinal Shape Reconstruction Based on {RGB-D} Data},
  author  = {Liebmann, Florentin and St{\"u}tz, Dominik and Suter, Daniel and Jecklin, Sascha and Snedeker, Jess G. and Farshad, Mazda and F{\"u}rnstahl, Philipp and Esfandiari, Hooman},
  journal = {Journal of Imaging},
  volume  = {7},
  number  = {9},
  pages   = {164},
  year    = {2021},
  doi     = {10.3390/jimaging7090164}
}
```