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

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}
}

Davinsteinsudas/BlindMap

BlindMap Checkpoints

Model Layout

Checkpoints

Installation

Data

Inference

Camera-Only

LiDAR-Only

Heterogeneous

Intended Use

Limitations

License

Citation

Davinsteinsudas/
BlindMap