Beam-Wise Statistical Background Subtraction for Static Roadside LiDAR: A Cross-Sensor Benchmark Study
Paper • 2608.14868 • Published
Datasets:
Tasks:
Other
Size:
n<1K
ArXiv:
Tags:
fiftyone
multimodal
lidar
roadside-perception
background-subtraction
intelligent-transportation-systems
Libraries:
FiftyOne
License:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: (... 466 chars omitted)
child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 454 chars omitted)
child 0, _id: struct<$oid: string>
child 0, $oid: string
child 1, filepath: string
child 2, tags: list<item: null>
child 0, item: null
child 3, _media_type: string
child 4, _rand: double
child 5, split: string
child 6, record_name: string
child 7, first_frame_id: int64
child 8, last_frame_id: int64
child 9, duration_s: double
child 10, message_count: int64
child 11, ouster_frames: int64
child 12, aeva_frames: int64
child 13, blickfeld_frames: int64
child 14, topics: list<item: string>
child 0, item: string
child 15, schemas: list<item: string>
child 0, item: string
child 16, has_pointcloud: bool
child 17, has_image: bool
child 18, has_gps: bool
child 19, has_imu: bool
child 20, has_logs: bool
child 21, _dataset_id: struct<$oid: string>
child 0, $oid: string
child 22, created_at: struct<$date: string>
child 0, $date: string
child 23, last_modified_at: struct<$date: string>
child 0, $date: string
group_media_types: null
info: null
app_config: null
classes: null
mask_targets: null
default_mask_targ
...
fields: list<item: (... 306 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: string
child 3, subfield: string
child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: null
child 3, subfield: null
child 4, fields: list<item: null>
child 0, item: null
child 5, db_field: string
child 6, description: null
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child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
child 5, db_field: string
child 6, description: null
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child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
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child 0, $oid: string
last_deletion_at: null
last_loaded_at: struct<$date: string>
child 0, $date: string
media_type: string
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child 0, item: null
default_classes: list<item: null>
child 0, item: null
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child 0, item: null
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to
{'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: (... 466 chars omitted)
child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 454 chars omitted)
child 0, _id: struct<$oid: string>
child 0, $oid: string
child 1, filepath: string
child 2, tags: list<item: null>
child 0, item: null
child 3, _media_type: string
child 4, _rand: double
child 5, split: string
child 6, record_name: string
child 7, first_frame_id: int64
child 8, last_frame_id: int64
child 9, duration_s: double
child 10, message_count: int64
child 11, ouster_frames: int64
child 12, aeva_frames: int64
child 13, blickfeld_frames: int64
child 14, topics: list<item: string>
child 0, item: string
child 15, schemas: list<item: string>
child 0, item: string
child 16, has_pointcloud: bool
child 17, has_image: bool
child 18, has_gps: bool
child 19, has_imu: bool
child 20, has_logs: bool
child 21, _dataset_id: struct<$oid: string>
child 0, $oid: string
child 22, created_at: struct<$date: string>
child 0, $date: string
child 23, last_modified_at: struct<$date: string>
child 0, $date: string
group_media_types: null
info: null
app_config: null
classes: null
mask_targets: null
default_mask_targ
...
fields: list<item: (... 306 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: string
child 3, subfield: string
child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: null
child 3, subfield: null
child 4, fields: list<item: null>
child 0, item: null
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
_id: struct<$oid: string>
child 0, $oid: string
last_deletion_at: null
last_loaded_at: struct<$date: string>
child 0, $date: string
media_type: string
frame_fields: list<item: null>
child 0, item: null
default_classes: list<item: null>
child 0, item: null
workspaces: list<item: null>
child 0, item: null
name: string
to
{'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for HighwayScene
Installation
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
dataset = load_from_hub("Voxel51/HighwayScene")
session = fo.launch_app(dataset)
Dataset Details
Dataset Description
HighwayScene is a synchronized multi-LiDAR roadside dataset introduced for the cross-sensor background subtraction benchmark in "Beam-Wise Statistical Background Subtraction for Static Roadside LiDAR: A Cross-Sensor Benchmark Study" (ITSC 2026). Three statically mounted sensors with different sensing principles observe the same highway construction-site scene simultaneously:
| Sensor | Principle | Rate |
|---|---|---|
| Ouster OS0 | Rotating time-of-flight | 10 Hz |
| Aeva Aeries II | FMCW | 10 Hz |
| Blickfeld QB2 | Solid-state time-of-flight | 5 Hz |
The scene features consistently high traffic volume under a legally enforced 40 km/h speed limit. The dataset supports research on background modeling, dynamic-point segmentation, and cross-sensor benchmarking for static roadside LiDAR.
This FiftyOne version converts the original protobuf .pb records to MCAP format so each episode can be explored interactively in the FiftyOne multimodal viewer, with all three sensor streams on a shared 20-second timeline.
- Curated by: Alexander Baumann, Marcel Voßhans, Thao Dang — Institute for Intelligent Systems, Esslingen University of Applied Sciences, Germany
- Funded by: [More Information Needed]
- Shared by: Institute for Intelligent Systems (IIS), Esslingen University of Applied Sciences
- Language(s): N/A
- License: CC BY-NC-SA 4.0 (non-commercial use only)
Dataset Sources
- Repository: https://huggingface.co/datasets/iis-esslingen/HighwayScene (original protobuf records)
- Paper: https://arxiv.org/abs/2608.14868
- Demo: https://highwayscene.github.io/
Uses
Direct Use
- Benchmarking beam-wise statistical background subtraction methods across heterogeneous LiDAR technologies
- Cross-sensor evaluation of static/dynamic point-cloud segmentation
- Research on roadside LiDAR perception: traffic monitoring, infrastructure-based sensing, inter-vehicle distance estimation
- Multi-modal LiDAR data exploration via the FiftyOne App's 3D tile and shared timeline
Out-of-Scope Use
- Moving-sensor or ego-vehicle applications — the benchmark assumes a statically mounted sensor with a fixed scan pattern
- Commercial use — the CC BY-NC-SA 4.0 license prohibits commercial applications
- Scenes with substantial long-term structural changes, changed sensor poses, or dynamically varying scan patterns
Dataset Structure
FiftyOne Dataset Topology
Media type: multimodal (MCAP files)
Sample count: 30 episodes
Splits: train (20), val (5), test (5), encoded as the split string field and queryable via dataset.match(fo.ViewField("split") == "test")
Each FiftyOne sample points to one .mcap file covering approximately 20 seconds of synchronized multi-sensor recording. The MCAP file contains three point-cloud topics (/ouster/points, /aeva/points, /blickfeld/points) and three static identity frame transforms on /tf_static. All topics use the foxglove.PointCloud schema and are rendered in the FiftyOne App's 3D tile on a shared timeline.
Sample Fields
| Field | FiftyOne type | Description |
|---|---|---|
filepath |
StringField |
Absolute path to the .mcap episode file |
split |
StringField |
Benchmark split: "train", "val", or "test" |
record_name |
StringField |
Original .pb filename stem (encodes recording date and global frame-ID range) |
first_frame_id |
IntField |
First global frame ID in this episode (1-based, dataset-wide) |
last_frame_id |
IntField |
Last global frame ID in this episode |
duration_s |
FloatField |
Episode duration in seconds (~19.9 s for 200-frame episodes) |
message_count |
IntField |
Total MCAP message count across all topics in this episode |
ouster_frames |
IntField |
Number of Ouster OS0 point-cloud messages (10 Hz; 200 per full episode) |
aeva_frames |
IntField |
Number of Aeva Aeries II point-cloud messages (10 Hz; 194–200 per episode) |
blickfeld_frames |
IntField |
Number of Blickfeld QB2 point-cloud messages (5 Hz; 95–101 per episode) |
topics |
ListField(StringField) |
MCAP topic names present in the episode |
schemas |
ListField(StringField) |
MCAP schema names used (foxglove.PointCloud, foxglove.FrameTransform) |
has_pointcloud |
BooleanField |
True for all 30 episodes |
has_image |
BooleanField |
False — no camera data in this dataset |
has_gps |
BooleanField |
False — no GPS data |
has_imu |
BooleanField |
False — no IMU data |
has_logs |
BooleanField |
False — no log messages |
MCAP Topic Structure (per episode)
| Topic | Schema | Rate | Per-point fields |
|---|---|---|---|
/ouster/points |
foxglove.PointCloud |
10 Hz | x, y, z (float32); intensity, ambient, reflectivity (float32); channel_id (float32) |
/aeva/points |
foxglove.PointCloud |
10 Hz | x, y, z (float32); intensity, ambient, reflectivity, velocity (float32, radial m/s) |
/blickfeld/points |
foxglove.PointCloud |
5 Hz | x, y, z (float32); reflectivity, channel_id, horizontal_id (float32) |
/tf_static |
foxglove.FrameTransform |
static | Identity transforms for ouster, aeva, blickfeld frames relative to world |
Typical point counts per frame: Ouster ~45K, Aeva ~92K, Blickfeld ~12K. Blickfeld has no intensity or ambient channel. Aeva's velocity field (radial velocity in m/s) is the unique FMCW capability enabling velocity-based dynamic-point labeling.
Ground Truth
No per-frame label files are stored in the FiftyOne dataset. The benchmark computes static/dynamic labels at evaluation time from parameters in metadata/ground_truth.yaml (in the original repository):
- Ouster and Blickfeld: lane-aligned 3D bounding-box ROIs define dynamic regions; any point inside is labeled dynamic.
- Aeva: a point is labeled dynamic if its measured radial velocity exceeds 1 m/s.
Parsing Decisions
- Original records are protobuf
.pbfiles decoded withhighwayscene-proto==1.0.0. Each.pb→ one.mcapusingfoxglove-sdk==0.26.0. - All uint16 auxiliary channels (intensity, ambient, reflectivity, channel_id, horizontal_id) are cast to float32 in the MCAP point-cloud buffers.
- Points with range ≤ 0.5 m are filtered (zero-return guard).
- Static transforms use
foxglove.FrameTransformwith no embedded timestamp, placing them in the viewer's static store for the full episode duration. - No extrinsic sensor-to-sensor calibration is provided by the dataset authors; all
world → sensortransforms are identity. - Blickfeld operates at 5 Hz: frames where the sensor is absent are simply omitted from the
/blickfeld/pointstopic.
Dataset Creation
Curation Rationale
Existing roadside LiDAR datasets focus on object detection or semantic segmentation with class-based labels, and are typically limited to a single sensor or scene. HighwayScene was created to fill the gap in systematic cross-sensor evaluation for static background subtraction — providing simultaneous recordings from three heterogeneous LiDAR technologies under real highway traffic conditions, with reproducible annotation strategies that do not require manual point-wise labeling.
Source Data
Data Collection and Processing
Data was recorded at a German highway construction site with a legally enforced 40 km/h speed limit. Sensors were mounted statically on roadside infrastructure. Recording rate is 10 Hz for the Ouster and Aeva sensors and 5 Hz for the Blickfeld. All streams are temporally synchronized. 5,998 global frames are captured across the full recording session (Feb 9, 2026), partitioned into train (frames 1–4000), val (4001–5000), and test (5001–5998) splits using a strict non-overlapping protocol.
Who are the source data producers?
Institute for Intelligent Systems (IIS), Esslingen University of Applied Sciences, Germany. Contact: alexander.baumann@hs-esslingen.de.
Annotations
Annotation process
Static/dynamic ground-truth labels are not stored per frame in the dataset records. Instead, two reproducible annotation strategies are defined in metadata/ground_truth.yaml:
Geometry-based (Ouster, Blickfeld): For each traffic lane, a static 3D bounding-box ROI aligned with the roadway is manually defined. Under empty-scene conditions no LiDAR returns fall inside these volumes. Since vehicles are constrained by concrete barriers, any return inside a lane volume originates from dynamic traffic. Points inside are labeled dynamic; all others static.
Velocity-based (Aeva): A point is labeled dynamic if its measured FMCW radial velocity exceeds 1 m/s. This strategy requires no scene geometry and illustrates the FMCW sensor's self-annotating capability.
Who are the annotators?
Annotation parameters (lane ROIs, ground planes) were defined by the dataset authors. No manual point-wise labeling was performed — labels are reproducibly derived from geometry and physics.
Personal and Sensitive Information
LiDAR point clouds do not record visually identifiable information (faces, license plates). No personal data is present in this dataset.
Citation
BibTeX:
@inproceedings{baumann2026beamwise,
author = {Alexander Baumann and Marcel Vo{\ss}hans and Thao Dang},
title = {Beam-Wise Statistical Background Subtraction for Static
Roadside {LiDAR}: A Cross-Sensor Benchmark Study},
booktitle = {IEEE International Conference on Intelligent Transportation
Systems (ITSC)},
year = {2026}
}
APA:
Baumann, A., Voßhans, M., & Dang, T. (2026). Beam-Wise Statistical Background Subtraction for Static Roadside LiDAR: A Cross-Sensor Benchmark Study. IEEE International Conference on Intelligent Transportation Systems (ITSC).
More Information
- Original dataset (protobuf format): https://huggingface.co/datasets/iis-esslingen/HighwayScene
- Background subtraction code: https://github.com/HighwayScene/roadside-lidar-background-subtraction
- Protobuf reader: https://github.com/HighwayScene/highwayscene-proto
- Project page: https://highwayscene.github.io/
Dataset Card Authors
Harpreet Sahota
Dataset Card Contact
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