Update README.md

README.md

@@ -13,99 +13,248 @@ language:
 [![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 was created to enable standardized benchmarking and systematic comparison of deep learning approaches for single-shot 3D depth reconstruction from fringe patterns.
+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 |
-| Depth reconstructions | 300 |
+| 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 |
+| Viewpoints per object | 6 (0°, 60°, 120°, 180°, 240°, 300°) |
 | Resolution | 960 × 960 pixels |
 | Measurement range | 1.5–1.8 m |
 | Ground truth method | 18-step phase shifting + Gray-code unwrapping |
-| Train / Val / Test split | 240 / 30 / 30 (object-level) |
+| Train / Val / Test split | 240 / 30 / 30 (object-level, 40/5/5 objects) |
 
-## Loading the Dataset
+## 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
 
-# Load a fringe image
-fringe = np.array(Image.open("train/fringe/sample.png").convert("L"), dtype=np.float32) / 255.0
+# --- 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 the corresponding ground truth depth map
-mat = sio.loadmat("train/depth/sample.mat")
+# 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]
 
-# Available depth keys depending on normalization:
-depth_raw                = mat["depthMap"]              # Raw depth in mm
-depth_global_normalized  = mat["depthMapMeters"]       # Depth in meters (raw / 1000)
-depth_individual_normalized = mat["depthMapNormalized"] # Per-sample [0, 1] normalized
+# Load the corresponding depth map
+depth = sio.loadmat(f"{dataset_dir}/train/depth/banana-a0.mat")["depthMap"]
 ```
 
-### Denormalizing Individual Normalized Depth
+### Denormalizing individual normalized depth
 
-Each sample has stored normalization parameters to recover metric depth:
+When using `training_data_depth_individual_normalized`, load the stored min/max to recover metric depth from model predictions:
 
 ```python
-# Load normalization parameters
-params = sio.loadmat("info_depth_params/train/depth/sample.mat")
+# 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 [0, 1] normalized prediction
-depth_mm = depth_normalized * (depth_max - depth_min) + depth_min
+# Recover depth in mm from a [0, 1] prediction
+depth_mm = prediction * (depth_max - depth_min) + depth_min
 ```
 
-## Dataset Structure
+### 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-ml-bench/
-├── train/                          # 240 samples (40 objects × 6 viewpoints)
-│   ├── fringe/                     # Input fringe pattern images (.png)
-│   └── depth/                      # Ground truth depth maps (.mat)
-├── val/                            # 30 samples (5 objects × 6 viewpoints)
-│   ├── fringe/
-│   └── depth/
-├── test/                           # 30 samples (5 objects × 6 viewpoints)
-│   ├── fringe/
-│   └── depth/
-└── info_depth_params/              # Per-sample normalization parameters
-    ├── train/depth/                # depth_min, depth_max per sample (.mat)
-    ├── val/depth/
-    └── test/depth/
+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
 ```
 
-### File Formats
+### Files per object-viewpoint
 
-- **Fringe images** (`.png`): Single-channel 8-bit grayscale, 960×960 pixels. Each image is the first frame of an 18-step phase-shifting sequence.
-- **Depth maps** (`.mat`): MATLAB files containing three depth representations per sample:
-  - `depthMap`: Raw depth in millimeters
-  - `depthMapMeters`: Global normalized (raw / 1000, in meters)
-  - `depthMapNormalized`: Individual normalized to [0, 1] using per-sample min/max
-- **Normalization parameters** (`.mat`): Contains `depth_min` and `depth_max` for each sample, required to recover metric depth from individual normalized predictions.
+| 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 |
 
-### Depth Normalization Strategies
+### Files in depth_information/
 
-The dataset includes three depth representations to support systematic evaluation of data representation strategies. Our benchmarking study found these lead to dramatically different model performance:
+Seven files per object-viewpoint, named `<object>-<angle>_<type>`:
 
-| Strategy | Key (`mat` file) | Range | Performance (Object MAE) |
-|----------|-----------------|-------|--------------------------|
-| Raw | `depthMap` | 0–2000 mm | 148.07 mm |
-| Global normalized | `depthMapMeters` | 0–2 m | 82.49 mm |
-| Individual normalized | `depthMapNormalized` | [0, 1] | 16.20 mm |
+| 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 |
 
-Individual normalization decouples shape from scale, achieving 9.1× improvement over raw depth. See the [paper](https://arxiv.org/abs/2601.08900) for full analysis.
+### 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 built 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.
+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 |
 |-----------|-------|
@@ -118,19 +267,11 @@ All data were generated using [VIRTUS-FPP](https://arxiv.org/abs/2509.22685), a
 
 ### Objects
 
-50 objects were sourced from two datasets:
-- **YCB Object Dataset**: Household objects (containers, tools, food items)
-- **NVIDIA Physical AI Warehouse**: Industrial components
-
-Objects span cylindrical containers, rectangular boxes, complex shapes (power drills, sprayguns), and industrial components, providing diversity in surface characteristics and morphological complexity.
-
-All objects use consistent matte material properties: roughness = 0.95, specular = 0.15, AO-to-diffuse = 0.95.
+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 per object with approximately 50% overlap between adjacent views:
-
-$$R_z(\theta_i) = \begin{bmatrix} \cos\theta_i & -\sin\theta_i & 0 \\ \sin\theta_i & \cos\theta_i & 0 \\ 0 & 0 & 1 \end{bmatrix}, \quad \theta_i = i \cdot 60°, \quad i = 0,1,...,5$$
+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
 
@@ -138,33 +279,31 @@ Ground truth depth maps were generated using an 18-step phase-shifting sequence
 
 ## Train/Val/Test Split
 
-The dataset is split at the **object level** to ensure evaluation on completely unseen geometries:
-
-| Split | Objects | Samples | Percentage |
-|-------|---------|---------|------------|
-| Train | 40 | 240 | 80% |
-| Val | 5 | 30 | 10% |
-| Test | 5 | 30 | 10% |
+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.
 
-**Important:** No object appears in more than one split. This prevents the model from memorizing object shapes seen during training and forces generalization to novel geometries.
+| 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
-- Developing and validating learning-based 3D reconstruction methods
-- Research into the fundamental limitations of single-shot depth recovery from structured light
+- 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 [paper](https://arxiv.org/abs/2601.08900) for discussion and future work on sim-to-real transfer.
+- **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 projector pattern**: Only the first fringe image from each 18-step sequence is used as model input. The remaining 17 patterns are used solely for ground truth generation.
+- **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 1.5–1.8 m. Performance at other distances is unknown.
 
 ## Citation
 
-If you use this dataset in your research, please cite:
+If you use this dataset, please cite:
 
 ```bibtex
 @article{lakshman2026comprehensive,
@@ -184,4 +323,4 @@ If you use this dataset in your research, please cite:
 
 ## Contact
 
-For questions or issues, please 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).
+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).