| --- |
| license: other |
| library_name: pytorch |
| tags: |
| - blindmap |
| - collaborative-perception |
| - cooperative-perception |
| - autonomous-driving |
| - v2x |
| - opv2v |
| - dair-v2x |
| - v2xset |
| - lidar |
| - camera |
| - 3d-object-detection |
| - pytorch |
| - opencood |
| model-index: |
| - name: BlindMap Checkpoints |
| results: |
| - task: |
| type: object-detection |
| name: Cooperative 3D Object Detection |
| dataset: |
| type: OPV2V |
| name: OPV2V |
| metrics: |
| - type: AP@0.3 |
| name: OPV2V Camera AP@0.3 |
| value: 70.03 |
| - type: AP@0.7 |
| name: OPV2V LiDAR AP@0.7 |
| value: 92.03 |
| - type: AP@0.7 |
| name: OPV2V Heterogeneous AP@0.7 |
| value: 79.43 |
| - task: |
| type: object-detection |
| name: Cooperative 3D Object Detection |
| dataset: |
| type: DAIR-V2X-C |
| name: DAIR-V2X-C |
| metrics: |
| - type: AP@0.3 |
| name: DAIR Camera AP@0.3 |
| value: 33.21 |
| - type: AP@0.7 |
| name: DAIR LiDAR AP@0.7 |
| value: 63.97 |
| - type: AP@0.7 |
| name: DAIR Heterogeneous AP@0.7 |
| value: 40.49 |
| - task: |
| type: object-detection |
| name: Cooperative 3D Object Detection |
| dataset: |
| type: V2XSet |
| name: V2XSet |
| metrics: |
| - type: AP@0.3 |
| name: V2XSet Camera AP@0.3 |
| value: 49.65 |
| - type: AP@0.7 |
| name: V2XSet LiDAR AP@0.7 |
| value: 78.71 |
| - type: AP@0.7 |
| name: V2XSet Heterogeneous AP@0.7 |
| value: 68.55 |
| --- |
| |
| # BlindMap Checkpoints |
|
|
| This repository contains checkpoints for **BlindMap**, a |
| communication-efficient collaborative perception method for deadline-constrained |
| V2X perception. |
|
|
| Paper: |
|
|
| > **Deadline-Aware Collaborative Perception via Receiver-Conditioned Utility |
| > Modeling** |
| > Zhenhan Zhu, Yanchao Zhao, Yihang Jiang, Hao Han, and Jie Wu. |
|
|
| Code release: |
|
|
| - Repository: [AlexZhu2000/BlindMap](https://github.com/AlexZhu2000/BlindMap) |
| - Repository: [NUAA-SmartSensing/BlindMap](https://github.com/NUAA-SmartSensing/BlindMap.git) |
| - Branch: `TMC` |
| - Commit used for this release: `075b2c7120277a619f41bc49a58e3905ffad7aa7` |
|
|
| ## Model Layout |
|
|
| ```text |
| . |
| ├── README.md |
| ├── LICENSE |
| ├── CHECKPOINT_MANIFEST.yaml |
| ├── SHA256SUMS |
| └── models |
| ├── dair |
| │ ├── camera |
| │ │ ├── config.yaml |
| │ │ └── net_epoch_bestval_at17.pth |
| │ ├── lidar |
| │ │ ├── blindmap_quality_result.txt |
| │ │ ├── config.yaml |
| │ │ └── net_epoch_bestval_at21.pth |
| │ └── heterogeneous |
| │ ├── config.yaml |
| │ └── net_epoch_bestval_at35.pth |
| ├── opv2v |
| │ ├── camera |
| │ │ ├── config.yaml |
| │ │ └── net_epoch_bestval_at17.pth |
| │ └── lidar_heterogeneous |
| │ ├── blindmap_quality_result.txt |
| │ ├── config.yaml |
| │ └── net_epoch_bestval_at37.pth |
| └── v2xset |
| ├── camera |
| │ ├── config.yaml |
| │ └── net_epoch_bestval_at19.pth |
| ├── lidar |
| │ ├── blindmap_quality_result.txt |
| │ ├── config.yaml |
| │ └── net_epoch_bestval_at19.pth |
| └── heterogeneous |
| ├── config.yaml |
| └── net_epoch_bestval_at23.pth |
| ``` |
|
|
| The DAIR-V2X-C and V2XSet releases use separate checkpoints for camera-only, |
| LiDAR-only, and heterogeneous settings. |
|
|
| OPV2V is organized differently: the LiDAR-only profile is evaluated from the |
| LiDAR branch inside the heterogeneous m1m2 checkpoint, following the HEAL-style |
| heterogeneous setup. Therefore OPV2V provides `camera` and |
| `lidar_heterogeneous` directories rather than a separate `lidar` directory. |
|
|
| ## Checkpoints |
|
|
| | Profile | Model directory | Checkpoint | SHA-256 | |
| |---|---|---|---| |
| | DAIR camera-only | `models/dair/camera` | `net_epoch_bestval_at17.pth` | `b95722f3b58a13a979315a84133da971415cdc9b6a94bb555b013bd834576d81` | |
| | DAIR LiDAR-only | `models/dair/lidar` | `net_epoch_bestval_at21.pth` | `99302097362f02dfb73f251a4bdf7d0c4e2d7ea9ebbc51422eb322d3f27e4429` | |
| | DAIR heterogeneous | `models/dair/heterogeneous` | `net_epoch_bestval_at35.pth` | `10511d4a9419f42051f01be2b9e6313b6718b2843f74795b5d9ceac7b200f7f5` | |
| | OPV2V camera-only | `models/opv2v/camera` | `net_epoch_bestval_at17.pth` | `33ddf54fe56d82d2719876a362b4838a4f21a14a068e9acd9c16066c41800fb3` | |
| | OPV2V LiDAR-only profile | `models/opv2v/lidar_heterogeneous` | `net_epoch_bestval_at37.pth` | `9e2570b64335c99241dc47fc3aa6cc9f97180a78b3ff5b54330cbf5a2072b7f3` | |
| | OPV2V heterogeneous | `models/opv2v/lidar_heterogeneous` | `net_epoch_bestval_at37.pth` | `9e2570b64335c99241dc47fc3aa6cc9f97180a78b3ff5b54330cbf5a2072b7f3` | |
| | V2XSet camera-only | `models/v2xset/camera` | `net_epoch_bestval_at19.pth` | `0dccab286798a380a78257ce7559e7e7978295a67e42d386e558f9f397e4edca` | |
| | V2XSet LiDAR-only | `models/v2xset/lidar` | `net_epoch_bestval_at19.pth` | `0a0553ae5e457071f39eb13dedf5f9627ad7d5da6a73898ecf0b9dbb6adcc230` | |
| | V2XSet heterogeneous | `models/v2xset/heterogeneous` | `net_epoch_bestval_at23.pth` | `60a73178b7178a18ea59e7beb8f0b4e7c1d115df3d7a97b9a4ebc511dc6d7bb9` | |
|
|
| The table lists SHA-256 checksums for checkpoint weights. The DAIR LiDAR and |
| V2XSet LiDAR values are taken from the corresponding Hugging Face LFS metadata. |
|
|
| ## Installation |
|
|
| Install the BlindMap codebase first. The checkpoints are designed for the |
| BlindMap/OpenCOOD-style `--model_dir` loader, where each checkpoint directory |
| contains both `config.yaml` and `net_epoch_bestval_at*.pth`. |
|
|
| ```bash |
| git clone --branch TMC https://github.com/AlexZhu2000/BlindMap.git |
| cd BlindMap |
| conda create -n blindmap python=3.8 |
| conda activate blindmap |
| pip install -r requirements.txt |
| python setup.py develop |
| python opencood/utils/setup.py build_ext --inplace |
| ``` |
|
|
| Use the same CUDA, PyTorch, and `spconv` versions described in the BlindMap |
| repository. The checkpoints were produced in the original BlindMap/OpenCOOD |
| environment and may not be compatible with arbitrary `spconv` versions. |
|
|
| ## Data |
|
|
| The datasets are not included. Download OPV2V, DAIR-V2X-C, and V2XSet from |
| their official providers and update the dataset paths in the selected |
| `config.yaml` before inference. |
|
|
| Typical keys to update: |
|
|
| ```yaml |
| root_dir: /path/to/dataset/train_or_train_json |
| validate_dir: /path/to/dataset/validate_or_val_json |
| test_dir: /path/to/dataset/test_or_val_json |
| data_dir: /path/to/dataset/root |
| ``` |
|
|
| Not every config uses all of these keys. |
|
|
| ## Inference |
|
|
| ### Camera-Only |
|
|
| ```bash |
| CUDA_VISIBLE_DEVICES=0 python opencood/tools/inference.py \ |
| --model_dir /path/to/BlindMap/models/opv2v/camera \ |
| --fusion_method intermediate \ |
| --modal 1 \ |
| --comm_volume_MB 1 \ |
| --range 102.4,102.4 |
| ``` |
|
|
| Use `models/dair/camera` with `--range 102.4,51.2` for DAIR camera-only, and |
| use `models/v2xset/camera` with `--range 102.4,102.4` for V2XSet camera-only. |
|
|
| ### LiDAR-Only |
|
|
| For OPV2V, use the LiDAR branch of the heterogeneous checkpoint: |
|
|
| ```bash |
| CUDA_VISIBLE_DEVICES=0 python opencood/tools/inference.py \ |
| --model_dir /path/to/BlindMap/models/opv2v/lidar_heterogeneous \ |
| --fusion_method intermediate \ |
| --modal 0 \ |
| --comm_volume_MB 1 \ |
| --range 102.4,102.4 |
| ``` |
|
|
| For DAIR-V2X-C and V2XSet, use their separate `lidar` checkpoint directories: `models/dair/lidar` with `--range 102.4,51.2`, and `models/v2xset/lidar` with `--range 102.4,102.4`. |
|
|
| ### Heterogeneous |
|
|
| ```bash |
| CUDA_VISIBLE_DEVICES=0 python opencood/tools/inference.py \ |
| --model_dir /path/to/BlindMap/models/opv2v/lidar_heterogeneous \ |
| --fusion_method intermediate \ |
| --modal 4 \ |
| --comm_volume_MB 1 \ |
| --range 102.4,102.4 |
| ``` |
|
|
| Use `models/dair/heterogeneous` with `--range 102.4,51.2` for DAIR |
| heterogeneous, and use `models/v2xset/heterogeneous` with |
| `--range 102.4,102.4` for V2XSet heterogeneous. |
|
|
| ## Intended Use |
|
|
| These checkpoints are intended for academic research on collaborative |
| perception, communication-efficient feature sharing, and reproducibility studies |
| on OPV2V, DAIR-V2X-C, and V2XSet. |
|
|
| They are not intended for deployment in autonomous vehicles or safety-critical |
| systems. |
|
|
| ## Limitations |
|
|
| - Results depend on the exact BlindMap code revision, dataset split, sensing |
| range, communication-budget accounting, and environment. |
| - OPV2V LiDAR-only results use the LiDAR branch of the OPV2V m1m2 checkpoint; |
| DAIR-V2X-C and V2XSet LiDAR-only results use separate LiDAR checkpoints. |
| - OPV2V and V2XSet are simulated datasets. DAIR-V2X-C is real-world but still |
| does not cover all deployment conditions. |
| - The model card provides representative metadata for provenance. Users should rerun |
| inference and report their own reproduced metrics. |
|
|
| ## License |
|
|
| The included `LICENSE` file follows the current BlindMap source license. It is |
| an academic research license with redistribution restrictions. If you intend to |
| redistribute, modify, or use these checkpoints outside academic research, obtain |
| the required permission from the rights holders first. |
|
|
| Dataset licenses apply separately. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{zhu2026deadline, |
| title = {Deadline-Aware Collaborative Perception via Receiver-Conditioned Utility Modeling}, |
| author = {Zhu, Zhenhan and Zhao, Yanchao and Jiang, Yihang and Han, Hao and Wu, Jie}, |
| journal = {IEEE Transactions on Mobile Computing}, |
| year = {2026}, |
| note = {Manuscript under review} |
| } |
| ``` |
|
|
