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
• Repository: NUAA-SmartSensing/BlindMap
• Branch: TMC
• Commit used for this release: 075b2c7120277a619f41bc49a58e3905ffad7aa7

Model Layout

.
├── 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.

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:

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

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:

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

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

@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}
}