--- 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.