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README.md

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----
-license: cc-by-nc-sa-4.0
----
+---
+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_001/
+│   │   ├── 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)
+├── 0112/
+└── ...
+```
+
+**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.