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
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:
@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.