README.md

metadata

license: apache-2.0
library_name: torch-pointcloud
tags:
  - point-cloud
  - 3d
  - pytorch
  - torch-pointcloud
  - second
  - object-detection
datasets:
  - kitti
model-index:
  - name: second.kitti.openpcdet
    results:
      - task:
          type: 3d-object-detection
        dataset:
          name: KITTI
          type: kitti
        metrics:
          - name: mAP
            type: map
            value: 66.26
          - name: AP/Car
            type: map
            value: 78.63
          - name: AP/Pedestrian
            type: map
            value: 52.98
          - name: AP/Cyclist
            type: map
            value: 67.16

Model card for second.kitti.openpcdet

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

Model Details

• Model Type: 3D object detection
• Model Stats:
  • Params (M): 5.3
  • Input channels: 4
  • Classes: 3
  • Features: 512
• Dataset: KITTI
• Metrics: mAP 66.26, AP/Car 78.63, AP/Pedestrian 52.98, AP/Cyclist 67.16
• Paper: SECOND: Sparsely Embedded Convolutional Detection
• Converted from: open-mmlab/OpenPCDet (Apache-2.0)
• Library: torch-pointcloud

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.kitti.openpcdet",
    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),
}
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{geiger2012kitti,
  title     = {Are we ready for Autonomous Driving? The {KITTI} Vision Benchmark Suite},
  author    = {Geiger, Andreas and Lenz, Philip and Urtasun, Raquel},
  booktitle = {CVPR},
  year      = {2012}
}

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