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A Group-Free 3D 3D object detection model (transformer detector over sampled object candidates). Trained on SUN RGB-D.
Trained and scored with the authors' SUN RGB-D protocol, which sizes each ground-truth box by the axis-aligned extent of the oriented box and keeps its heading. These scores do not compare with detectors scored against the oriented boxes themselves.
pip install torch-pointcloud
import torch
import torch_pointcloud as tp
from torch_pointcloud.utils.data import collate
model, info = tp.create_model(
"groupfree3d-l6-o256.sunrgbd.ze-liu",
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),
"color": torch.rand(num_points, 3) * 255,
}
data = info["transform"](sample)
data = collate([data])
with torch.no_grad():
out = model(data.get("x"), data["pos"], data["batch"])
with torch.no_grad():
features = model.forward_features(data.get("x"), data["pos"], data["batch"]) # 288 channels
@inproceedings{liu2021groupfree,
title = {Group-Free 3D Object Detection via Transformers},
author = {Ze Liu and Zheng Zhang and Yue Cao and Han Hu and Xin Tong},
booktitle = {ICCV},
year = {2021}
}
@inproceedings{song2015sunrgbd,
title = {{SUN RGB-D}: A {RGB-D} Scene Understanding Benchmark Suite},
author = {Song, Shuran and Lichtenberg, Samuel P. and Xiao, Jianxiong},
booktitle = {CVPR},
year = {2015}
}
@software{dujardin2026pytorchpointcloud,
author = {Arthur Dujardin},
title = {PyTorch PointCloud},
year = {2026},
doi = {10.5281/zenodo.22159632},
url = {https://github.com/arthurdjn/pytorch-pointcloud},
}