File size: 2,541 Bytes
0536576 d1ba90c 0536576 5c148ed 0536576 5c148ed 0536576 ac1c63e 0536576 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | ---
license: apache-2.0
library_name: torch-pointcloud
tags:
- point-cloud
- 3d
- pytorch
- torch-pointcloud
- 3detr
- object-detection
datasets:
- sunrgbd
model-index:
- name: 3detr.sunrgbd.fair
results:
- task:
type: 3d-object-detection
dataset:
name: SUN RGB-D
type: sunrgbd
metrics:
- name: mAP@0.25
type: map
value: 58.08
- name: mAP@0.5
type: map
value: 29.64
---
# 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:** mAP@0.25 58.08, mAP@0.5 29.64 (reference 58.0)
- **Paper:** [An End-to-End Transformer Model for 3D Object Detection](https://arxiv.org/abs/2109.08141)
- **Converted from:** [facebookresearch/3detr](https://github.com/facebookresearch/3detr) (Apache-2.0)
- **Library:** [torch-pointcloud](https://github.com/arthurdjn/pytorch-pointcloud)
## Install
```bash
pip install torch-pointcloud
```
## Usage
```python
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
```python
with torch.no_grad():
features = model.forward_features(data.get("x"), data["pos"], data["batch"]) # 256 channels
```
## Citation
```bibtex
@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},
}
```
|