sberrio/Mixed-Signals-V2X

Mixed Signals V2X — 3D Object Detection Dataset

Point clouds and 3D bounding-box labels for the Mixed Signals dataset — a diverse, real-world dataset for heterogeneous LiDAR V2X collaboration (ICCV 2025). Collected at a busy intersection with 3 connected vehicles and a roadside unit (RSU) carrying two LiDARs — 5 LiDAR sensors per synchronized frame.

This repository accompanies the Codabench detection challenge.

Contents

The dataset is delivered as per-segment .tar archives (not loose files), so downloads stay fast and resumable. One archive = one ~30 s scene.

train/  mini_<N>.tar     # 29 archives — point clouds + odometry + labels
val/    mini_<N>.tar     #  4 archives — point clouds + odometry (labels withheld)
test/   test-XXXX.tar    # ~6 pooled archives — anonymized bundles (see Splits)

Each train / val archive extracts to the native layout:

PointClouds/            # per-agent point clouds (ASCII .pcd: x y z intensity)
  mini_<N>/
    top_<k>_<ts>.pcd    dome_<k>_<ts>.pcd     # RSU lidars
    003_<k>_<ts>.pcd    004_<k>_<ts>.pcd    laser_<k>_<ts>.pcd   # vehicles
Odometry/               # per-vehicle map-frame poses (nav_msgs/Odometry CSVs)
  mini_<N>/ odometry_{003,004,laser}.csv
labels/                 # 3D box ground truth — TRAINING ARCHIVES ONLY
  mini_<N>/ mini_<N>_<k>.txt

Each test archive extracts to self-contained anonymized bundles — one folder per frame, containing the 5 agent clouds plus a precomputed transforms.json:

<token>/ 003.pcd 004.pcd dome.pcd laser.pcd top.pcd  transforms.json

Downloading & using

Download only the segments you need and extract in place — the archives rebuild the folder layout the dev-kit reads:

# one train segment
hf download sberrio/Mixed-Signals-V2X train/mini_6.tar --repo-type dataset --local-dir .
tar xf train/mini_6.tar          # -> PointClouds/mini_6 + Odometry/mini_6 + labels/mini_6

# the whole test split (a handful of archives)
hf download sberrio/Mixed-Signals-V2X --repo-type dataset --include "test/*" --local-dir .
for f in test/test-*.tar; do tar xf "$f" -C test/; done   # -> test/<token>/...

Then load frames with the starting kit's msig_devkit.py (native backend for train/val, bundle backend for test) — no loader changes are needed.

Dataset Viewer is not available for this repo (the data ships as .tar archives). This is a predictions-only competition; participants use the dev-kit, not the Viewer.

Splits

37 segments (mini_1 … mini_37), each a ~30 s synchronized sequence.

Split	Segments	Labels here?
Train	29 segments (all except those below)	✅ yes
Validation	mini_9, 18, 27, 36	❌ withheld (Development leaderboard)
Test	mini_4, 5, 17, 21	❌ withheld (Final leaderboard)

Point clouds are provided for all 37 segments; only train labels are public. Validation/test labels are held out for the competition leaderboards. (Test = the dataset's official test split, {4,5,17,21}.)

Label format

labels/mini_<N>/mini_<N>_<syncIndex>.txt — one file per synchronized frame. The frame id mini_<N>_<syncIndex> pairs with the point clouds: sync step k of segment mini_N is the clouds top_k…, dome_k…, 003_k…, 004_k…, laser_k….

Each line is one 3D box, 8 whitespace-separated columns:

cls  cx  cy  cz  dx  dy  dz  yaw

Field	Meaning	Units
cls	class: 1=Vehicle, 2=Bike, 3=Pedestrian	—
cx cy cz	box centre, top (RSU) ego frame	metres
dx dy dz	length, width, height	metres
yaw	heading (rotation about +z)	radians

Empty files are frames with no labelled objects. Boxes are restricted to x, y ∈ [-51.2, 51.2] m and contain >5 aggregated lidar points. The 10 fine-grained annotation classes are grouped into the 3 meta-classes above.

Training counts: 29 segments · 8,394 frames · 124,668 boxes (Vehicle 48,721 · Bike 30,893 · Pedestrian 45,054).

Coordinate frame

All boxes are in the top RSU LiDAR (ego) frame. The two RSU lidars are static; the three vehicles' poses are in Odometry/. Predictions for the competition must also be in the top frame.

License & citation

Use is governed by the Mixed Signals dataset terms (academic / non-commercial; see the dataset website). If you use this data, please cite:

@inproceedings{luo2025mixedsignals,
  title     = {Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous
               LiDAR V2X Collaboration},
  author    = {Luo, Katie Z. and Dao, Minh-Quan and Liu, Zhenzhen and
               Campbell, Mark and Chao, Wei-Lun and Weinberger, Kilian Q. and
               Malis, Ezio and Fr\'emont, Vincent and Hariharan, Bharath and
               Shan, Mao and Worrall, Stewart and Berrio Perez, Julie Stephany},
  booktitle = {Proceedings of the IEEE/CVF International Conference on
               Computer Vision (ICCV)},
  year      = {2025}
}