| --- |
| pretty_name: "Mixed Signals V2X: Collaborative 3D Object Detection" |
| license: cc-by-nc-sa-4.0 |
| tags: |
| - 3d-object-detection |
| - lidar |
| - point-cloud |
| - autonomous-driving |
| - v2x |
| - collaborative-perception |
| size_categories: |
| - 10K<n<100K |
| |
| |
| viewer: false |
| --- |
| |
| # Mixed Signals V2X: Collaborative 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, for 5 LiDAR sensors per synchronized |
| frame. |
|
|
| This repository accompanies a collaborative 3D object detection competition on |
| [Codabench](https://www.codabench.org/), built on the |
| [Mixed Signals dataset](https://mixedsignalsdataset.cs.cornell.edu/). |
|
|
| ## Competition |
|
|
| A predictions-only collaborative 3D detection challenge (Vehicle / Bike / |
| Pedestrian) scored by BEV rotated-box mAP. Two phases: Development (public validation split, |
| Aug 1 to 28 2026), then Final (held-out test split, Aug 29 to |
| Sep 4 2026). Download the data here, train locally, and submit per-frame |
| prediction files on Codabench. See the competition's starting kit for the |
| submission format. |
|
|
| ## Contents |
|
|
| The dataset is delivered as per-segment `.tar` archives (not loose files), so |
| downloads stay fast and resumable. One archive is one ~30 s scene. |
|
|
| ``` |
| train/ mini_<N>.tar # 29 archives: point clouds + odometry + labels |
| val/ mini_<N>.tar # 4 archives: point clouds + odometry |
| test/ test-XXXX.tar # ~6 pooled archives: anonymized bundles (see Splits) |
| ``` |
|
|
| Each train or 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 and using |
|
|
| Download only the segments you need and extract in place. The archives rebuild |
| the folder layout the dataloader reads: |
|
|
| ```bash |
| # 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_dataloader.py` (native backend for |
| train/val, bundle backend for test). No loader changes are needed. |
|
|
| ## Splits |
|
|
| 37 segments (`mini_1 … mini_37`), each a ~30 s synchronized sequence. |
|
|
| | Split | Segments | Labels here? | |
| |:--|:--|:--:| |
| | Train | 29 segments | ✅ yes | |
| | Validation | 4 segments | ❌ withheld (Development leaderboard) | |
| | Test | 4 segments | ❌ withheld (Final leaderboard) | |
|
|
| Point clouds are provided for all 37 segments. Only train labels are public. |
| Validation and test labels are held out for the competition leaderboards. |
|
|
| ## 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, with 8 whitespace-separated columns: |
|
|
| ``` |
| cls cx cy cz dx dy dz yaw |
| ``` |
|
|
| | Field | Meaning | Units | |
| |:--|:--|:--| |
| | `cls` | class: `1`=Vehicle, `2`=Bike, `3`=Pedestrian | n/a | |
| | `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 and citation |
|
|
| Released under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
| (attribution, non-commercial, share-alike). If you use this data, please cite: |
|
|
| ```bibtex |
| @inproceedings{luo2025mixed, |
| 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} |
| } |
| ``` |
|
|
