3D-MOOD

3D-MOOD predicts three-dimensional boxes from a single image.

Task: 3d detection
Instalasi: pip install "libreyolo[hf]"
Tier dukungan: Tier sejajar, sejak v1.6.0. Permukaan produk terpisah dengan factory dan kontraknya sendiri.
Proyek upstream: 3D-MOOD oleh ETH Zurich, Computer Vision and Geometry Lab, Apache-2.0. Makalah, sumber
Lisensi: Kode MIT, bobot Apache-2.0. Penggunaan komersial

Install

bash

pip install "libreyolo[hf]"

Predict

Python

from libreyolo import Libre3DMOOD, SAMPLE_IMAGEimport numpy as npfrom PIL import Image # Requires the separately installed upstream runtime.model = Libre3DMOOD(device="cpu")# Rough 3x3 pinhole guess; replace with measured calibration for the original image.w, h = Image.open(SAMPLE_IMAGE).sizeintrinsics = np.array([[w, 0, w / 2], [0, w, h / 2], [0, 0, 1]], dtype=np.float32)result = model.predict(SAMPLE_IMAGE, intrinsics=intrinsics, text="person")print(result.boxes3d)

3D-MOOD performs open-set detection using text labels and original-image camera intrinsics. Install its separate upstream runtime and provide runtime_path and runtime_python when needed.

result.boxes3d holds centers, dimensions and wxyz quaternions in camera coordinates, plus confidence, class and intrinsics. Plotting projects cuboids into the image. Training, validation, tracking and export are not supported. See 3D detection.

Licensing

Periksa lisensi di repositori Hugging Face untuk bobot tertentu yang diunduh. Setiap checkpoint di organisasi LibreYOLO memiliki lisensi, dan lisensinya tidak selalu sama dalam satu family. Repositori tersebut adalah sumber resmi. Ringkasan di bawah menjelaskan ketentuan yang berlaku saat halaman ini terakhir diverifikasi.

Ini adalah deskripsi lisensi yang terlibat, bukan nasihat hukum. Jika jawabannya penting secara komersial, baca sendiri lisensinya dan mintalah nasihat dari penasihat hukum Anda.

Karya asli: 3D-MOOD, ETH Zurich, Computer Vision and Geometry Lab
Lisensi upstream: Apache-2.0
Sumber upstream: github.com/cvg/3D-MOOD
Kode LibreYOLO: MIT
Bobot: Apache-2.0, dipublikasikan ulang di huggingface.co/LibreYOLO
Interpretasi: The source and model publisher declare Apache-2.0.

Citation

@InProceedings{Yang_2025_ICCV,
    author    = {Yang, Yung-Hsu and Piccinelli, Luigi and Segu, Mattia and Li, Siyuan and Huang, Rui and Fu, Yuqian and Pollefeys, Marc and Blum, Hermann and Bauer, Zuria},
    title     = {3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2025},
    pages     = {7429-7439}
}

Disalin dari blok sitasi penulis di raw.githubusercontent.com/cvg/3D-MOOD/main/README.md.