Datasets:

NeurIPS-2026-PRISM
/

PRISM-Dataset

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
pretty_name: PRISM
size_categories:
  - 10K<n<100K

PRISM: Polarimetric Road-surface Intelligent Sensing and Measurement Dataset

Anonymous submission to NeurIPS 2026 Evaluations & Datasets Track. Author identities and the camera-ready release URL will be revealed at the camera-ready stage.

PRISM is a polarimetric road-surface dataset and benchmark of 47,098 time-synchronized frames combining trichromatic linear polarization, co-boresighted RGB, 128-channel LiDAR, and RTK-GNSS/INS, captured from an in-vehicle sensor rig across proving-ground and open-road environments under clear, overcast, rainy, foggy, and snowy conditions. It is designed as an evaluative testbed for measuring whether, and under which conditions, polarimetric channels add discriminative signal to RGB on road-surface perception.

Why PRISM

RGB cameras struggle on the road surface itself: pavement is nearly texture-less at the centimetre scale, and dry, wet, and icy asphalt can look photometrically identical. Polarization is the natural complement, since Fresnel optics ties the angle of linear polarization (AoLP) to surface-normal azimuth and the degree of linear polarization (DoLP) to refractive index, but no public dataset has so far paired visible-light polarization with the geometric and semantic ground truth required to test whether this physical promise translates into measurable gains on real roads.

Dataset at a glance

Property	Value
Frames	47,098 (time-synchronized, multi-modal)
Sessions (datasets)	41, anonymized as `0106_dataset`, `0112_dataset`, …
Modalities	RGB, four-orientation linear polarization (0°/45°/90°/135°), accumulated LiDAR scan, vehicle state
Image resolution	2448 × 2048, 12-bit
LiDAR	Ouster OS1, 128 channels
Pose	RTK-corrected, tightly-coupled GNSS/INS
Synchronization	GPS-PPS hardware trigger
Environments	Proving-ground (35.2%), Open-road urban/rural/highway (64.8%)
Weather	Clear (65.1%), Rainy (14.7%), Overcast (13.5%), Foggy (5.0%), Snow (1.7%)
Materials	Asphalt, Concrete, Belgian block, Gravel, Other
Surface states	Dry, Damp, Wet, Slush, Snow-covered
Elevation supervision	Derived from `lidar_accum_scan/*.pcd` (LiDAR-inertial SLAM-accumulated point clouds)
Total size	≈ 1.59 TB
License	CC-BY-NC-SA 4.0

Sensor platform

Color-polarization camera: Lucid Triton 5.0 MP, Sony IMX264MYR, on-chip 2×2 polarizer super-pixels at {0°, 45°, 90°, 135°} over a Bayer CFA.
RGB camera: Lucid Triton 5.0 MP, Sony IMX264 (same sensor family without polarizer array, co-boresighted with the polarization camera).
LiDAR: Ouster OS1, 128 channels.
GNSS/INS: NovAtel PwrPak7, RTK-corrected, tightly-coupled.

The two cameras are drawn from the same sensor family to minimise modality-comparison confounds. Camera–camera and camera–LiDAR extrinsics were calibrated with the MATLAB Camera Calibration and Lidar–Camera Calibration toolboxes, and cross-validated by comparing direct vs composed extrinsics. Polarimetric fidelity through the front windshield was characterised with a controlled LCD-monitor protocol (see paper, Appendix F).

Repository layout

The dataset is distributed as per-session ZIP files, organised at the top level by train / validation split:

PRISM-Dataset/
├── README.md
├── labels.json                          # frame-level material/state labels (whole dataset)
├── calibration/                         # camera intrinsics + inter-sensor extrinsics
│   ├── 0106_calibration.yaml
│   ├── …
│   └── 0327_calibration.yaml
├── train/
│   ├── 0106_dataset.zip
│   ├── 0112_dataset.zip
│   ├── 0119_1_dataset.zip
│   ├── …
│   └── 0327_11_dataset.zip
└── val/
    ├── 0112_dataset.zip          # last 30 % of one snow session (intra-session split)
    ├── 0119_1_dataset.zip
    └── …

labels.json and calibration/ are kept outside the per-session ZIPs so they can be inspected without unpacking any archive.

Some session names appear in both train/ and val/: those are sessions that were split within a recording (intra-session split) to preserve training-set coverage of rare classes such as slush and snow-covered, which originate from a small number of capture sites. See paper Appendix E for the full split protocol.

Inside each ZIP, the layout is:

{session_anon}/                              # e.g. 0106_dataset/
├── 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
├── sequence_002/
│   └── …
└── vehicle_state/                           # session-level (NOT per-sequence)

File naming. All files share the same nanosecond Unix timestamp as the filename stem (e.g. 1736200123456789012.png). RGB, the four polarisation images, and the corresponding accumulated LiDAR scan for the same instant share a stem, which is also how the train / val split is internally applied to intra-session sessions.

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. The Stokes-to-AoLP/DoLP formulas are given in the paper (Section 3) and a reference implementation is included in the code release.

LiDAR. The lidar_accum_scan/ directory contains LiDAR-inertial SLAM-accumulated point clouds (one PCD per frame) produced by the pipeline described in paper Section 4.3. These accumulated clouds are already deskewed, aligned to a common ground frame, and ICP-refined on static ground segments, so they can be used directly. They serve as the source of dense road-surface elevation supervision for Track 2: elevation BEV is rasterised from the PCD onto the 10 cm × 10 cm grid at training and evaluation time. Single-sweep raw LiDAR is not released at this time.

Vehicle state. vehicle_state/ is a session-level directory (not per-sequence) containing RTK-INS pose and synchronised vehicle-bus signals.

Labels. Frame-level surface material and state labels are released as a single labels.json at the repository root, indexed by (session_id, sequence_id, timestamp).

Calibration. Camera intrinsics and inter-sensor extrinsics (RGB ↔ polarisation, camera ↔ LiDAR) are released as YAML files under the calibration/ directory at the repository root.

Privacy. Faces and licence plates throughout the released imagery are replaced in-place by Gaussian blur (OpenCV cv2.GaussianBlur, 31×31 kernel) on the raw polariser-resolved intensities. The road region is unaffected. See paper Appendix K.

Anonymised session naming

To support double-blind review, the original session folder names (which encode location and date) are remapped to chronological-anonymised names. The full mapping is provided as dataset_name_remapping.json alongside the code release, and will be revealed at the camera-ready stage. Anonymised names follow the pattern {date_index}_{seq_index}_dataset where date_index is a chronological index of the recording day.

Benchmark tracks

PRISM defines two benchmark tracks, both evaluated under a shared five-variant input ablation crossing RGB, monochromatic / trichromatic polarisation, and their combinations:

ID	Variant	Channels	Role
(a)	RGB	3	RGB-intensity baseline
(b)	Mono-polar	3	DoLP + (sin 2φ, cos 2φ) AoLP, luminance-averaged
(c)	Tri-polar	9	Per-channel R/G/B DoLP + AoLP
(d)	RGB + Mono-polar	3 \| 3	Two-stream fusion
(e)	RGB + Tri-polar	3 \| 9	Two-stream fusion (full polarimetric)

Track 1 — Road-surface condition classification. Joint per-frame prediction of surface material ∈ {asphalt, concrete, Belgian block, gravel} and surface state ∈ {dry, damp, wet, slush, snow-covered}. Reported metrics: accuracy, macro-precision, macro-recall, macro-F1 (primary).

Track 2 — Road-surface elevation estimation. Dense BEV elevation prediction on a 10 cm × 10 cm grid covering longitudinal x ∈ [7, 12] m and lateral y ∈ [−2, 2] m (one-lane near-field preview window for active suspension). Reported metrics: MAE, RMSE, and the fraction of cells whose absolute error exceeds 0.5 / 1.0 / 2.0 cm.

Both tracks are further analysed under stratification by surface state, surface material, and weather to localise where polarimetric channels matter most.

Splits

Partition	Frames
Train	≈ 84.7 %
Validation	≈ 15.3 %
Test	Released at camera-ready, scene-disjoint where possible

Frames captured during surface transitions, where two or more material or state classes are simultaneously present in a single image, are excluded (≈ 13 % of all collected frames) to ensure unambiguous single-label annotation. Train / validation partitions are defined at the session level where possible, with two exceptions to preserve coverage of rare classes: sparse special-surface sessions (gravel, dust-room, plastic-bump-only) use within-session splitting, and single-source surface states (snow-covered, slush) allocate the last 30 % of the session to validation. See paper Appendix E.

Quick start

from huggingface_hub import snapshot_download
import zipfile, os, json

# 1. Pull labels, calibration, and a single train session
local = snapshot_download(
    repo_id="NeurIPS-2026-PRISM/PRISM-Dataset",
    repo_type="dataset",
    allow_patterns=[
        "labels.json",
        "calibration/*.yaml",
        "train/0106_dataset.zip",
    ],
)

# 2. Unpack the session
with zipfile.ZipFile(os.path.join(local, "train/0106_dataset.zip")) as zf:
    zf.extractall("prism_local")

# 3. Walk a sequence
seq = "prism_local/0106_dataset/sequence_001"
print(sorted(os.listdir(seq)))
# → ['lidar_accum_scan', 'polar', 'rgb']

# 4. Lookup labels for one frame
labels = json.load(open(os.path.join(local, "labels.json")))
ts = sorted(os.listdir(f"{seq}/rgb"))[0].replace(".png", "")
print(labels["0106_dataset"]["sequence_001"][ts])
# → {"material": "asphalt", "state": "dry"}

Deriving elevation BEV from lidar_accum_scan/. Each .pcd is an already-accumulated, ego-aligned ground-frame point cloud. To produce the BEV elevation grid used by Track 2, project the points onto a 10 cm × 10 cm grid covering longitudinal x ∈ [7, 12] m and lateral y ∈ [−2, 2] m and take a per-cell statistic (mean / median) over the points falling inside each cell, with a per-cell valid mask flagging cells with too few returns. A reference implementation is included in the code release.

To pull the full dataset (≈ 1.59 TB), drop the allow_patterns argument and ensure you have sufficient disk space.

Intended use and out-of-scope use

Intended. Research on road-surface perception, polarimetric sensing, multi-modal fusion, physics-informed learning, and shape-from-polarization in unconstrained outdoor settings.

Out of scope.

Direct deployment in safety-critical systems without further validation.
Re-identification of individuals or vehicles. Faces and licence plates are detected and Gaussian-blurred (31×31 kernel) prior to release; the road region is unaffected.
Night-time perception. PRISM sessions are predominantly daytime, since a polariser halves incoming flux and polarimetric SNR degrades at low illumination.
Absolute polarimetric measurement beyond the fidelity bounds documented in Appendix F of the paper. Applications requiring absolute AoLP recovery should add per-session optical-path calibration.

Known limitations

Long tail. Rare classes (snow weather 1.7 %, snow-covered state 3.1 %, gravel material 2.1 %, foggy weather 5.0 %) have low absolute frequency. Stratified results on these classes should be read as indicative.
Geographic scope. All sessions were collected in a single country; asphalt composition, Belgian-block prevalence, and weather distribution are location-specific.
Label granularity. Surface material and state labels are at frame level, not pixel level. Pixel-level labels on a subset are a planned extension.
Ground-truth precision. Elevation derived from accumulated LiDAR is strong but imperfect on specular wet surfaces and under GNSS multipath; absolute claims below ≈ 0.5 cm should be interpreted in light of the proving-ground qualitative validation in Appendix I of the paper.

Citation

@inproceedings{prism2026,
  title     = {PRISM: Polarimetric Road-surface Intelligent Sensing and Measurement Dataset},
  author    = {Anonymous},
  booktitle = {Advances in Neural Information Processing Systems 39 (NeurIPS 2026), Evaluations and Datasets Track},
  year      = {2026},
  note      = {Anonymous submission}
}

License and terms of use

PRISM is released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC-BY-NC-SA 4.0).

By downloading or using PRISM, you agree to:

Attribution. Cite the PRISM paper in any publication that uses the dataset.
Non-commercial use only. Academic and non-commercial research use, derivative works, and redistribution with attribution are permitted under the same licence. Commercial use is not permitted without a separate agreement with the authors.
Share-alike. Derivative works must be released under CC-BY-NC-SA 4.0.
No re-identification. Do not attempt re-identification of any individual or vehicle that may inadvertently appear in the imagery, even though faces and licence plates have been blurred.

Contact

For dataset-related questions during the review period, please use the OpenReview submission discussion. Author contact will be revealed at the camera-ready stage.