Point cloud object detection
Collection
12 items • Updated
A PointPillars 3D object detection model (pillar encoder with a 2D backbone). Trained on KITTI.
pip install torch-pointcloud
import torch
import torch_pointcloud as tp
from torch_pointcloud.utils.data import collate
model, info = tp.create_model(
"pointpillars.kitti.openpcdet",
task="detection",
pretrained=True,
return_info=True,
)
model = model.eval()
# 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")
with torch.no_grad():
out = model(data["voxel"], data["pos_voxel"], data["voxel_num_points"], data["batch"])
with torch.no_grad():
features = model.forward_features(
data["voxel"],
data["pos_voxel"],
data["voxel_num_points"],
data["batch"],
) # 384 channels
@inproceedings{lang2019pointpillars,
title = {PointPillars: Fast Encoders for Object Detection from Point Clouds},
author = {Alex H. Lang and Sourabh Vora and Holger Caesar and Lubing Zhou and Jiong Yang and Oscar Beijbom},
booktitle = {CVPR},
year = {2019}
}
@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},
}