---
license: mit
pretty_name: Baseline NLOS Histogram Dataset
language:
- zxx
tags:
- lidar
- nlos
- time-of-flight
- histograms
- synthetic-data
size_categories:
- 10K<n<100K
configs:
- config_name: synthetic
  default: true
  data_files:
  - split: train
    path: metadata/synthetic_train.csv
  - split: validation
    path: metadata/synthetic_validation.csv
  - split: test
    path: metadata/synthetic_test.csv
- config_name: experimental
  data_files:
  - split: unassigned
    path: metadata/experimental.csv
---

# Baseline NLOS histogram dataset

38,000 synthetic and 180 real experimental histogram samples for research on
binary classification in non-line-of-sight (NLOS) sensing. Each HDF5 file contains
one `32 × 32 × 64` histogram. Model weights are not included.

## Contents

| Source | Negative (0) | Positive (1) | Total |
|---|---:|---:|---:|
| Synthetic | 19,000 | 19,000 | 38,000 |
| Experimental | 90 | 90 | 180 |

Synthetic samples include `clean` (1,000 per class) and `noise_1` through
`noise_36` (500 per class per condition). The condition names are identifiers,
not calibrated noise magnitudes. Experimental samples retain their original
acquisition groups in nine balanced folders.

```text
data/
  synthetic_data/
    tmf8829_32x32_negative/{clean,noise_1,...,noise_36}/histogram/*.h5
    tmf8829_32x32_positive/{clean,noise_1,...,noise_36}/histogram/*.h5
  experiment_data/
    fold_1/ ... fold_9/
    manifest.csv
metadata/
  synthetic_train.csv
  synthetic_validation.csv
  synthetic_test.csv
  experimental.csv
  validation_summary.json
manifest.csv
SHA256SUMS
examples/load_sample.py
scripts/validate_dataset.py
requirements.txt
```

All manifest paths are relative to the **repository root**, including paths in
`data/experiment_data/manifest.csv`. The root manifest indexes every sample;
metadata CSVs select subsets of those same records. The Hugging Face viewer
configurations expose these metadata rows, not decoded histogram tensors.

## Labels and schema

`label=0` means the source dataset's **negative** class; `label=1` means its
**positive** class. Labels are inherited from the source filenames and manifest.
The exact physical criterion defining target presence/absence is not documented
in the packaged source metadata and requires author confirmation.

| Field | Meaning |
|---|---|
| HDF5 key | `stamped_histogram` |
| Shape | `(32, 32, 64)` |
| Axes | Two spatial pixel axes, followed by histogram bin index |
| Synthetic dtype | `int64` |
| Experimental dtype | `uint32` |
| Values | Stored histogram counts; no normalization applied during packaging |

Spatial orientation, bin width, timing offset, and physical range units have not
been verified. Do not infer them from the tensor shape. All experimental files
have root attributes `image_height=32`, `image_width=32`, `bin_number=64`,
`range_min=0.0`, and `range_max=1890.0`; the range units are unspecified.

Manifest columns: `sample_id` (unique path-derived identifier), `path`, `label`,
`label_name`, `source`, `fold`, `condition`, `split`, `h5_key`, `height`, `width`,
`bins`, `dtype`, and `sha256`. Synthetic `fold` fields are empty. Experimental
`condition` matches the fold identifier.

## Splits and experimental groups

The synthetic split assignments are preserved from the original training run:
26,600 train, 5,700 validation, and 5,700 test samples (70/15/15). The source
`val` name is exported as `validation`. No new random split was generated.
The source configuration specifies seed 42.

| Folder | Original group | Positive | Negative |
|---|---|---:|---:|
| fold_1 | 1_experiment | 10 | 10 |
| fold_2 | 2_experiment | 10 | 10 |
| fold_3 | 3_experiment | 10 | 10 |
| fold_4 | 4_experiment | 10 | 10 |
| fold_5 | 5_experiment | 10 | 10 |
| fold_6 | addition_test/left | 10 | 10 |
| fold_7 | addition_test/right | 10 | 10 |
| fold_8 | addition_test/long | 10 | 10 |
| fold_9 | addition_test/short | 10 | 10 |

Experimental samples have `split=unassigned`: these folders preserve acquisition
groups and do not themselves specify a cross-validation protocol. For evaluation,
state whether real data are held out entirely or used in group-based cross-validation.
Keep each acquisition group together when constructing a new experimental split.
The historical use of these samples for model selection has not been audited.

The supplied synthetic split is a historical baseline, not a guarantee of
independence across related clean/noisy scenes. Scene-family identifiers are not
included, so scene-level leakage cannot be ruled out. Fit preprocessing statistics
on training data only and report the split and preprocessing used.

## Loading a sample

```bash
python -m pip install -r requirements.txt
python examples/load_sample.py --source experimental --fold fold_1
python examples/load_sample.py --source synthetic --split train
```

After publication, replace `PeizhaoEdinburgh/Occlusion_Based_NLOS` with the repository ID:

```bash
python examples/load_sample.py --repo-id PeizhaoEdinburgh/Occlusion_Based_NLOS --source experimental --fold fold_1
```

Use `--revision COMMIT_HASH` to pin the remote version. The example downloads the
manifest and one sample, verifies its SHA-256 hash and schema, and loads it with
`h5py`. It returns the stored values without training-specific preprocessing.
For direct access:

```python
import h5py

with h5py.File("data/experiment_data/fold_1/1_positive_histogram_capture_1.h5", "r") as f:
    histogram = f["stamped_histogram"][()]  # shape (32, 32, 64)
```

## Provenance and limitations

The source collection is identified as `LameDar2/tmf8829_32x32` and the baseline
run as
`20260726_212202_tmf8829_36noise_seed_lhs_plus30_36_global_log_count_coarse_multiscale_h128_d4_aug_standard_hist_coarse_multiscale_mlp`.
The synthetic directory names identify the TMF8829 sensor setting. Complete
simulation parameters, sensor calibration, acquisition dates, physical setup
measurements, and noise-condition definitions are not supplied here.

The experimental files were moved into the fold structure without changing their
contents. Two spelling errors in filenames (`positve`, `negtive`) were corrected.
Original experiment group names are recorded above. Machine-specific source paths
are excluded from the portable manifests.

This dataset is intended for research. Balanced class counts do not establish
real-world prevalence or generalization to other sensors and environments.
No paper citation or author attribution has been supplied yet.

## Integrity

```bash
python scripts/validate_dataset.py
# Alternatively, verify file bytes only from the repository root:
sha256sum --check SHA256SUMS
```

The validator checks all 38,180 sample hashes and HDF5 schemas, complete manifest
coverage, split consistency, and the nine balanced experimental groups.

## License and publication status

The repository specifies the MIT license. Author confirmation is still needed
for attribution, label semantics, physical units, and acquisition/generation details.

Dataset repository: [PeizhaoEdinburgh/Occlusion_Based_NLOS](https://huggingface.co/datasets/PeizhaoEdinburgh/Occlusion_Based_NLOS).
Local runtime and environment directories such as `.omx/` and `.venv/` are excluded
from the published package.

The metadata layout follows Hugging Face's
[manual configuration documentation](https://huggingface.co/docs/hub/datasets-manual-configuration).
Remote loading uses the documented
[Hub download API](https://huggingface.co/docs/huggingface_hub/guides/download).