Hugging Face's logo

torch-pointcloud
/
second-multihead.nuscenes.openpcdet

Object Detection
Safetensors
PyTorch
torch-pointcloud
point-cloud
3d
second
Model card Files
xet
 Community
second-multihead.nuscenes.openpcdet / README.md
arthurdjn's picture
arthurdjn
Update model card for second-multihead.nuscenes.openpcdet
f8cb20d verified
preview code
|
Raw
History Blame Contribute Delete
2.83 kB
metadata 
license: apache-2.0
library_name: torch-pointcloud
tags:
  - point-cloud
  - 3d
  - pytorch
  - torch-pointcloud
  - second
  - object-detection
datasets:
  - nuscenes

Model card for second-multihead.nuscenes.openpcdet

A SECOND 3D object detection model (sparse convolutional voxel detector). Trained on nuScenes.

Model Details

Install 

pip install torch-pointcloud

This checkpoint also needs spconv, which needs a build matching your torch and CUDA: see the installation guide.

Usage 

import torch
import torch_pointcloud as tp
from torch_pointcloud.utils.data import collate

model, info = tp.create_model(
    "second-multihead.nuscenes.openpcdet",
    task="detection",
    pretrained=True,
    return_info=True,
)
model = model.cuda().eval()  # GPU-only kernels

# synthetic sample with the keys a dataset provides
num_points = 8192
sample = {
    "pos": torch.randn(num_points, 3),
    "intensity": torch.rand(num_points, 1),
    "timestamp": torch.zeros(num_points, 1),
}
data = info["transform"](sample)
data = collate([data], batch_from="pos_voxel")
data = {key: value.cuda() for key, value in data.items()}

with torch.no_grad():
    out = model(data["voxel"], data["pos_voxel"], data["voxel_num_points"], data["batch"])

Feature extraction 

with torch.no_grad():
    features = model.forward_features(
        data["voxel"],
        data["pos_voxel"],
        data["voxel_num_points"],
        data["batch"],
    )  # 512 channels

Citation 

@article{yan2018second,
  title   = {{SECOND}: Sparsely Embedded Convolutional Detection},
  author  = {Yan, Yan and Mao, Yuxing and Li, Bo},
  journal = {Sensors},
  volume  = {18},
  number  = {10},
  pages   = {3337},
  year    = {2018}
}

@inproceedings{caesar2020nuscenes,
  title   = {nuScenes: A multimodal dataset for autonomous driving},
  author  = {Holger Caesar and Varun Bankiti and Alex H. Lang and Sourabh Vora and Venice Erin Liong and Qiang Xu and Anush Krishnan and Yu Pan and Giancarlo Baldan and Oscar Beijbom},
  booktitle = {CVPR},
  year    = {2020}
}

@software{dujardin2026pytorchpointcloud,
  author  = {Arthur Dujardin},
  title   = {PyTorch PointCloud},
  year    = {2026},
  doi     = {10.5281/zenodo.22159632},
  url     = {https://github.com/arthurdjn/pytorch-pointcloud},
}