3detr.sunrgbd.fair

Model card for 3detr.sunrgbd.fair

A 3DETR 3D object detection model (end-to-end set-prediction transformer detector). Trained on SUN RGB-D.

Model Details

• Model Type: 3D object detection
• Model Stats:
  • Params (M): 7.3
  • Classes: 10
  • Features: 256
• Dataset: SUN RGB-D
• Metrics: [email protected] 58.08, [email protected] 29.64 (reference 58.0)
• Paper: An End-to-End Transformer Model for 3D Object Detection
• Converted from: facebookresearch/3detr (Apache-2.0)
• Library: torch-pointcloud

Install

pip install torch-pointcloud

Usage

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

model, info = tp.create_model(
    "3detr.sunrgbd.fair",
    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"])

Feature extraction

with torch.no_grad():
    features = model.forward_features(data.get("x"), data["pos"], data["batch"])  # 256 channels

Citation

@inproceedings{misra2021detr3d,
  title   = {An End-to-End Transformer Model for 3D Object Detection},
  author  = {Ishan Misra and Rohit Girdhar and Armand Joulin},
  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},
}