README.md

---
license: mit
task_categories:
- depth-estimation
- feature-extraction
language:
- en
---

# FPP-ML-Bench: Fringe Projection Profilometry Benchmarking Dataset

[![arXiv](https://img.shields.io/badge/arXiv-2601.08900-b31b1b.svg)](https://arxiv.org/abs/2601.08900)
[![SPIE Photonics West](https://img.shields.io/badge/SPIE%20Photonics%20West-Presentation-blue)](https://spie.org/photonics-west/presentation/Comprehensive-machine-learning-benchmarking-for-fringe-projection-profilometry-with-photorealistic/13904-1)
[![GitHub](https://img.shields.io/badge/GitHub-Code-green)](https://github.com/AnushLak/fpp-ml-bench)

The first open-source, photorealistic synthetic dataset for single-shot fringe projection profilometry (FPP), generated using [VIRTUS-FPP](https://arxiv.org/abs/2509.22685) in NVIDIA Isaac Sim. This dataset enables standardized benchmarking and systematic comparison of deep learning approaches for single-shot 3D depth reconstruction from fringe patterns.

## Dataset Summary

| Property | Value |
|----------|-------|
| Total fringe images | 15,600 (52 per viewpoint × 6 viewpoints × 50 objects) |
| Depth reconstructions | 300 (6 viewpoints × 50 objects) |
| Objects | 50 |
| Viewpoints per object | 6 (0°, 60°, 120°, 180°, 240°, 300°) |
| Resolution | 960 × 960 pixels |
| Measurement range | 1.5–2.1 m |
| Ground truth method | 18-step phase shifting + Gray-code unwrapping |
| Train / Val / Test split | 240 / 30 / 30 (object-level, 40/5/5 objects) |

## Repository Layout

The dataset is organized into two top-level directories serving different purposes:

```
fpp-ml-bench/
├── training_datasets/          # Pre-split, ready-to-train data (plug and play)
└── fpp_synthetic_dataset/      # Full raw dataset per object (complete scans + all metadata)
```

`training_datasets/` is what you need if you want to train models directly. `fpp_synthetic_dataset/` is the complete raw dataset with all phase, mesh, and reconstruction data per object.

---

## training_datasets/

Pre-split into train/val/test at the object level. Contains six dataset variants covering all combinations of normalization strategy and background handling, plus the normalization parameter files needed for individual normalization.

```
training_datasets/
├── training_data_depth_raw/
│   ├── train/
│   │   ├── fringe/             # 240 fringe images (full background)
│   │   └── depth/              # 240 raw depth .mat files
│   ├── val/
│   │   ├── fringe/             # 30 fringe images
│   │   └── depth/              # 30 raw depth .mat files
│   └── test/
│       ├── fringe/             # 30 fringe images
│       └── depth/              # 30 raw depth .mat files
├── training_data_depth_global_normalized/      # same structure, global normalized depth
├── training_data_depth_individual_normalized/  # same structure, [0,1] normalized depth
├── training_data_bgremoved_depth_raw/          # background pixels zeroed in fringe input
├── training_data_bgremoved_depth_global_normalized/
├── training_data_bgremoved_depth_individual_normalized/
└── info_depth_params/          # per-sample min/max for individual normalization
    ├── train/depth/
    ├── val/depth/
    └── test/depth/
```

### Loading training data

```python
import scipy.io as sio
from PIL import Image
import numpy as np

# --- Pick a dataset variant ---
# Full background (recommended):
#   training_data_depth_raw
#   training_data_depth_global_normalized
#   training_data_depth_individual_normalized        <-- recommended
# Background removed (for ablation study):
#   training_data_bgremoved_depth_raw
#   training_data_bgremoved_depth_global_normalized
#   training_data_bgremoved_depth_individual_normalized

dataset_dir = "training_datasets/training_data_depth_individual_normalized"

# Load a fringe image
fringe = np.array(
    Image.open(f"{dataset_dir}/train/fringe/banana-a0.png").convert("L"),
    dtype=np.float32
) / 255.0  # normalize to [0, 1]

# Load the corresponding depth map
depth = sio.loadmat(f"{dataset_dir}/train/depth/banana-a0.mat")["depthMap"]
```

### Denormalizing individual normalized depth

When using `training_data_depth_individual_normalized`, load the stored min/max to recover metric depth from model predictions:

```python
# Load normalization parameters (mirror the split and filename)
params = sio.loadmat(
    "training_datasets/info_depth_params/train/depth/banana-a0.mat"
)
depth_min = float(params["depth_min"])
depth_max = float(params["depth_max"])

# Recover depth in mm from a [0, 1] prediction
depth_mm = prediction * (depth_max - depth_min) + depth_min
```

### Dataset variants

The six `training_data_*` folders cover the full experimental matrix from the paper:

| Folder | Fringe input | Depth target | Object MAE (mm) |
|--------|-------------|--------------|-----------------|
| `training_data_depth_raw` | Full | Raw (mm) | 148.07 |
| `training_data_depth_global_normalized` | Full | Meters | 82.49 |
| `training_data_depth_individual_normalized` | Full | [0, 1] | **16.20** |
| `training_data_bgremoved_depth_raw` | BG zeroed | Raw (mm) | 437.40 |
| `training_data_bgremoved_depth_global_normalized` | BG zeroed | Meters | 598.40 |
| `training_data_bgremoved_depth_individual_normalized` | BG zeroed | [0, 1] | 45.01 |

Background removal degrades performance across all normalizations. See the [paper](https://arxiv.org/abs/2601.08900) for full analysis.

---

## fpp_synthetic_dataset/

The complete raw dataset. Each of the 50 objects has its full 6-viewpoint scan with all intermediate and final outputs from VIRTUS-FPP. All depth representations live in a single flat `depth_information/` folder.

```
fpp_synthetic_dataset/
├── depth_information/                              # All depth data, flat (2100 files)
│   ├── banana-a0_raw_depth.mat
│   ├── banana-a0_raw_depth.png
│   ├── banana-a0_global_normalized_depth.mat
│   ├── banana-a0_global_normalized_depth.png
│   ├── banana-a0_individual_normalized_depth.mat
│   ├── banana-a0_individual_normalized_depth.png
│   ├── banana-a0_individual_normalized_depth_params.mat
│   ├── banana-a60_raw_depth.mat                    # ... next viewpoint
│   └── ...                                         # 7 files × 300 samples
│
├── banana/                                         # object folder (50 total)
│   ├── A0/                                         # viewpoint (6 per object)
│   │   ├── A_0.png                                 # fringe images (52 per viewpoint)
│   │   ├── A_1.png
│   │   ├── ...
│   │   ├── A_51.png
│   │   ├── banana-a0.ply                           # ground truth mesh
│   │   ├── wrapped_phase.mat                       # wrapped phase map
│   │   ├── unwrapped_phase.mat                     # unwrapped phase map
│   │   ├── reconstruction.fig                      # MATLAB figure
│   │   ├── reconstruction.png                      # rendered reconstruction
│   │   ├── mask.csv                                # object mask
│   │   ├── x.csv                                   # point cloud X coordinates
│   │   ├── y.csv                                   # point cloud Y coordinates
│   │   └── z.csv                                   # point cloud Z coordinates
│   ├── A60/                                        # next viewpoint, same structure
│   ├── A120/
│   ├── A180/
│   ├── A240/
│   └── A300/
├── battery/                                        # next object, same structure
└── ...                                             # 50 objects total
```

### Files per object-viewpoint

| File | Format | Description |
|------|--------|-------------|
| `A_0.png` – `A_51.png` | PNG (960×960, grayscale) | 52-frame fringe pattern sequence. `A_0.png` is used as model input in the benchmarking study. |
| `<object>-<angle>.ply` | PLY | Ground truth 3D mesh |
| `wrapped_phase.mat` | MAT | Wrapped phase map from phase-shifting algorithm |
| `unwrapped_phase.mat` | MAT | Temporally unwrapped phase (Gray-code) |
| `mask.csv` | CSV | Binary object mask |
| `x.csv`, `y.csv`, `z.csv` | CSV | Point cloud coordinates (mm) |
| `reconstruction.png` | PNG | Rendered depth reconstruction |
| `reconstruction.fig` | FIG | MATLAB figure of reconstruction |

### Files in depth_information/

Seven files per object-viewpoint, named `<object>-<angle>_<type>`:

| Suffix | Format | Description |
|--------|--------|-------------|
| `_raw_depth.mat` | MAT | Depth in millimeters |
| `_raw_depth.png` | PNG | Visualization of raw depth |
| `_global_normalized_depth.mat` | MAT | Depth in meters (raw / 1000) |
| `_global_normalized_depth.png` | PNG | Visualization of global normalized depth |
| `_individual_normalized_depth.mat` | MAT | Depth normalized to [0, 1] per sample |
| `_individual_normalized_depth.png` | PNG | Visualization of individual normalized depth |
| `_individual_normalized_depth_params.mat` | MAT | `depth_min` and `depth_max` for denormalization |

### Loading from fpp_synthetic_dataset

```python
import scipy.io as sio
from PIL import Image
import numpy as np

object_name = "banana"
viewpoint   = "A0"
angle_tag   = "a0"  # lowercase, used in depth_information filenames

base = "fpp_synthetic_dataset"

# --- Full fringe sequence ---
fringes = [
    np.array(Image.open(f"{base}/{object_name}/{viewpoint}/A_{i}.png").convert("L"))
    for i in range(52)
]

# --- Ground truth mesh ---
# banana-a0.ply  (use open3d or trimesh)
import trimesh
mesh = trimesh.load(f"{base}/{object_name}/{viewpoint}/{object_name}-{angle_tag}.ply")

# --- Phase maps ---
wrapped   = sio.loadmat(f"{base}/{object_name}/{viewpoint}/wrapped_phase.mat")
unwrapped = sio.loadmat(f"{base}/{object_name}/{viewpoint}/unwrapped_phase.mat")

# --- Point cloud ---
import pandas as pd
x = pd.read_csv(f"{base}/{object_name}/{viewpoint}/x.csv").values
y = pd.read_csv(f"{base}/{object_name}/{viewpoint}/y.csv").values
z = pd.read_csv(f"{base}/{object_name}/{viewpoint}/z.csv").values

# --- Depth maps (from depth_information) ---
raw_depth = sio.loadmat(
    f"{base}/depth_information/{object_name}-{angle_tag}_raw_depth.mat"
)["depthMap"]

ind_depth = sio.loadmat(
    f"{base}/depth_information/{object_name}-{angle_tag}_individual_normalized_depth.mat"
)["depthMap"]

params = sio.loadmat(
    f"{base}/depth_information/{object_name}-{angle_tag}_individual_normalized_depth_params.mat"
)
depth_min, depth_max = float(params["depth_min"]), float(params["depth_max"])
```

---

## Data Acquisition

### Virtual FPP System

All data were generated using [VIRTUS-FPP](https://arxiv.org/abs/2509.22685), a physics-based virtual FPP system in NVIDIA Isaac Sim. The pipeline integrates OptiX ray tracing for photorealistic rendering, PhysX for physics, and USD for 3D scene composition. The projector is modeled via the inverse camera model, enabling accurate fringe projection at arbitrary distances without hardware constraints.

| Parameter | Value |
|-----------|-------|
| Camera focal length | 50 cm |
| Camera resolution | 960 × 960 pixels |
| Projector intensity | 40 nits |
| Projector offset | 0.1 m below, 0.125 m left of camera |
| Stereo reprojection error | 0.056 pixels |
| Projector reprojection error | 0.049 pixels |

### Objects

50 objects sourced from the [YCB Object Dataset](https://ycb-objects.github.io/) and [NVIDIA Physical AI Warehouse](https://developer.nvidia.com/physical-ai). The collection spans cylindrical containers, rectangular boxes, complex shapes (power drills, sprayguns), and industrial components. All objects use consistent matte material properties: roughness = 0.95, specular = 0.15, AO-to-diffuse = 0.95.

### Multi-View Acquisition

Each object was rotated about the vertical axis in 60° increments, yielding 6 viewpoints (A0, A60, A120, A180, A240, A300) with approximately 50% overlap between adjacent views.

### Ground Truth Generation

Ground truth depth maps were generated using an 18-step phase-shifting sequence combined with Gray-code temporal unwrapping and triangulation. This provides perfect ground truth geometry free from the measurement errors inherent to physical systems.

## Train/Val/Test Split

The split is performed at the **object level**—no object appears in more than one split. This forces models to generalize to entirely unseen geometries rather than memorizing shapes seen during training.

| Split | Objects | Samples (objects × 6 viewpoints) |
|-------|---------|----------------------------------|
| Train | 40 | 240 |
| Val | 5 | 30 |
| Test | 5 | 30 |

## Intended Uses

- Benchmarking deep learning architectures for single-shot FPP depth estimation
- Evaluating data representation and loss function strategies for fringe-to-depth learning
- Research into phase unwrapping, depth refinement, and multi-view fusion
- Studying fundamental limitations of single-shot depth recovery from structured light

## Limitations

- **Synthetic only**: All data is rendered in simulation. Domain gap to real-world FPP systems has not been characterized. See the [paper](https://arxiv.org/abs/2601.08900) for discussion on sim-to-real transfer.
- **Material properties**: All objects use identical matte materials. Specular, translucent, or highly reflective surfaces are not represented.
- **Single-shot input**: Only `A_0.png` (first fringe image) is used as model input in the benchmarking study. The remaining 51 patterns are available for alternative formulations (e.g., multi-frame input).
- **Fixed measurement range**: All objects are scanned at 1.5–2.1 m. Performance at other distances is unknown.

## Citation

If you use this dataset, please cite:

```bibtex
@article{lakshman2026comprehensive,
  title={Comprehensive Machine Learning Benchmarking for Fringe Projection Profilometry with Photorealistic Synthetic Data},
  author={Lakshman S, Anush and Haroon, Adam and Li, Beiwen},
  journal={arXiv preprint arXiv:2601.08900},
  year={2026}
}

@article{haroon2025virtus,
  title={VIRTUS-FPP: virtual sensor modeling for fringe projection profilometry in NVIDIA Isaac Sim},
  author={Haroon, Adam and Lakshman, Anush and Balasubramaniam, Badrinath and Li, Beiwen},
  journal={arXiv preprint arXiv:2509.22685},
  year={2025}
}
```

## Contact

Questions or issues: open an issue on [GitHub](https://github.com/AnushLak/FPP-ML-Benchmarking) or contact [anushlak@iastate.edu](mailto:anushlak@iastate.edu) or [aharoon@iastate.edu](mailto:aharoon@iastate.edu).