README.md

---
license: cc-by-nc-sa-4.0
task_categories:
- image-classification
- depth-estimation
language:
- en
tags:
- autonomous-driving
- polarization
- polarimetric-imaging
- road-surface
- multi-modal
- lidar
- benchmark
- sample
pretty_name: PRISM Sample
size_categories:
- n<1K
---

# PRISM Sample: Polarimetric Road-surface Intelligent Sensing and Measurement Dataset

> **Anonymous submission to NeurIPS 2026 Evaluations & Datasets Track.**

This is a representative **sample** of the PRISM dataset, designed to enable reviewers and researchers to inspect data quality without downloading the full ~1.6 TB dataset.

## Why a sample dataset?

The full PRISM dataset contains **47,098 time-synchronized frames** across 41 sessions. This sample provides:
- **Quick quality inspection**: Download < 4 GB instead of 1.6 TB
- **Representative coverage**: All surface types and conditions
- **Reproducible sampling**: Exact methodology documented in `SAMPLING_MANIFEST.json`

## Sample at a glance

| Property | Value |
|---|---|
| Frames | ~30 (3 frames × 10 datasets) |
| Datasets | 10 representative sessions |
| Modalities | RGB, four-orientation polarization (0°/45°/90°/135°), accumulated LiDAR, vehicle state |
| Image resolution | 2448 × 2048, 12-bit |
| Surface types | Asphalt, Concrete, Belgian block, Gravel |
| Surface conditions | Dry, Damp, Wet, Slush, Snow-covered |
| Size | < 4 GB |
| Format | ZIP files (`train.zip`, `val.zip`) |

## Sampling strategy

### Selection criteria

We selected **10 representative datasets** from 41 total sessions to cover:
- **All surface types**: asphalt, concrete, belgian_block, gravel
- **All road conditions**: dry, damp, wet, slush, snow_covered
- **Both train and validation splits**: Including intra-session splits

Each dataset includes **1 sequence** with **3 uniformly sampled frames**, providing temporal coverage while maintaining manageable file size.

### Representative datasets

| Dataset | Surface Type | Condition | Split | Frames |
|---------|-------------|-----------|-------|--------|
| 0106 | asphalt | dry | train | 3 |
| 0112 | asphalt | snow_covered | train (intra-split) | 3 |
| 0129_1 | asphalt | damp | train | 3 |
| 0318_9 | asphalt | wet | train | 3 |
| 0124 | asphalt | slush | train | 3 |
| 0128_1 | concrete | snow_covered | val | 3 |
| 0128_3 | concrete | damp | val | 3 |
| 0327_3 | belgian_block | dry | val | 3 |
| 0328_5 | belgian_block | snow_covered | val | 3 |
| 0327_9 | gravel | dry | val | 3 |

### Temporal sampling

For each sequence:
- **3 frames** uniformly sampled across the full sequence duration
- **Vehicle state data**: ±100ms window around each sampled frame (~60 files per sequence @ 100Hz)
- **All sensor modalities**: RGB, polarimetric (0°, 45°, 90°, 135°), LiDAR accumulated scan

### Privacy protection

Privacy measures identical to the full dataset:
- **RGB images**: Faces and licence plates replaced by Gaussian blur (OpenCV `cv2.GaussianBlur`, 31×31 kernel)
- **Polarimetric images**: Masked where corresponding RGB masks exist
- **Vehicle state**: GPS coordinates included (public roads only)

## Repository layout

The sample dataset is distributed as **two ZIP files** matching the full dataset structure:

```
PRISM-Dataset-Sample/
├── README.md                            # This file
├── train.zip                            # Training split samples
└── val.zip                              # Validation split samples
```

Inside each ZIP (`train.zip` or `val.zip`):

```
train/  (or val/)
├── 0106/                                # Anonymized session name
│   ├── sequence_006/
│   │   ├── rgb/                         # *.png   RGB images
│   │   ├── polar/
│   │   │   ├── 0d/                      # *.png   polariser at  0°
│   │   │   ├── 45d/                     # *.png   polariser at 45°
│   │   │   ├── 90d/                     # *.png   polariser at 90°
│   │   │   └── 135d/                    # *.png   polariser at 135°
│   │   └── lidar_accum_scan/            # *.pcd   accumulated scan
│   └── vehicle_state/                   # *.txt   session-level (NOT per-sequence)
├── 0124/
└── ...
```

**File naming.** All files share the same timestamp as filename stem (e.g., `1736200123_456.png` for images, `1736200123_456.pcd` for LiDAR, `1736200123_456.txt` for vehicle state). The format is `{seconds}_{milliseconds}` derived from Unix nanosecond timestamps.

**Polarisation.** PRISM ships **raw four-orientation polariser-resolved intensities** rather than pre-computed Stokes / AoLP / DoLP maps. This keeps the release closer to the sensor and lets users compute polarimetric quantities under their own conventions.

**LiDAR.** The `lidar_accum_scan/` directory contains **LiDAR-inertial SLAM-accumulated point clouds** (one PCD per frame). These accumulated clouds are already deskewed, aligned to a common ground frame, and ICP-refined on static ground segments.

**Vehicle state.** `vehicle_state/` is a session-level directory (not per-sequence) containing RTK-INS pose and synchronised vehicle-bus signals at 100 Hz. Each `.txt` file contains 29 comma-separated values:

```
timestamp, latitude, longitude, altitude,
roll, pitch, yaw,
velocity_x, velocity_y, velocity_z,
acceleration_x, acceleration_y, acceleration_z,
angular_velocity_x, angular_velocity_y, angular_velocity_z,
... (additional vehicle dynamics data)
```

## Full dataset

This sample represents approximately **0.06%** of the full PRISM dataset.

| Property | Full Dataset | Sample |
|---|---|---|
| Sessions | 41 | 10 |
| Frames | 47,098 | ~30 |
| Size | ~1.6 TB | <4 GB |
| Coverage | All conditions | Representative conditions |

**Full dataset:** [https://huggingface.co/datasets/NeurIPS-2026-PRISM/PRISM-Dataset](https://huggingface.co/datasets/NeurIPS-2026-PRISM/PRISM-Dataset)

The script applies uniform temporal sampling to each selected sequence and copies files according to the per-file masked priority logic documented in the full dataset README.

## License

CC-BY-NC-SA 4.0

## Citation

If you use this dataset, please cite:

```bibtex
@article{prism2026,
  title={{PRISM}: Polarimetric Road-surface Intelligent Sensing and Measurement Dataset},
  author={Anonymous},
  journal={NeurIPS Datasets and Benchmarks Track},
  year={2026}
}
```

## Contact

For questions about this sample dataset or the full PRISM dataset, please open an issue on the dataset repository during the review period.