---
license: cc-by-4.0
task_categories:
- other
tags:
- 3d
- computer-vision
- orientation-estimation
---

# Orient Anything V2 Dataset

[**Project Page**](https://orient-anythingv2.github.io/) | [**Paper**](https://huggingface.co/papers/2601.05573) | [**GitHub**](https://github.com/SpatialVision/Orient-Anything-V2)

**Orient Anything V2** is an enhanced foundation model for unified understanding of object 3D orientation and rotation from single or paired images. This repository contains the training data (final rendering data) used for the model.

## Sample Usage

Below is a snippet to run inference using the model and data logic, as found in the [official GitHub repository](https://github.com/SpatialVision/Orient-Anything-V2):

```python
import numpy as np
from PIL import Image
import torch
import tempfile
import os

from paths import *
from vision_tower import VGGT_OriAny_Ref
from inference import *
from app_utils import *

mark_dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16
# device = 'cuda:0'
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

if os.path.exists(LOCAL_CKPT_PATH):
    ckpt_path = LOCAL_CKPT_PATH
else:
    from huggingface_hub import hf_hub_download
    ckpt_path = hf_hub_download(repo_id="Viglong/Orient-Anything-V2", filename=HF_CKPT_PATH, repo_type="model", cache_dir='./', resume_download=True)

model = VGGT_OriAny_Ref(out_dim=900, dtype=mark_dtype, nopretrain=True)
model.load_state_dict(torch.load(ckpt_path, map_location='cpu'))
model.eval()
model = model.to(device)
print('Model loaded.')

@torch.no_grad()
def run_inference(pil_ref, pil_tgt=None, do_rm_bkg=True):
    if pil_tgt is not None:
        if do_rm_bkg:
            pil_ref = background_preprocess(pil_ref, True)
            pil_tgt = background_preprocess(pil_tgt, True)
    else:
        if do_rm_bkg:
            pil_ref = background_preprocess(pil_ref, True)

    try:
        ans_dict = inf_single_case(model, pil_ref, pil_tgt)
    except Exception as e:
        print("Inference error:", e)
        raise gr.Error(f"Inference failed: {str(e)}")

    def safe_float(val, default=0.0):
        try:
            return float(val)
        except:
            return float(default)

    az = safe_float(ans_dict.get('ref_az_pred', 0))
    el = safe_float(ans_dict.get('ref_el_pred', 0))
    ro = safe_float(ans_dict.get('ref_ro_pred', 0))
    alpha = int(ans_dict.get('ref_alpha_pred', 1))

    if pil_tgt is not None:
      rel_az = safe_float(ans_dict.get('rel_az_pred', 0))
      rel_el = safe_float(ans_dict.get('rel_el_pred', 0))
      rel_ro = safe_float(ans_dict.get('rel_ro_pred', 0))

      print("Relative Pose: Azi",rel_az,"Ele",rel_el,"Rot",rel_ro)

image_ref_path = 'assets/examples/F35-0.jpg'
image_tgt_path = 'assets/examples/F35-1.jpg' # optional

image_ref = Image.open(image_ref_path).convert('RGB')
image_tgt = Image.open(image_tgt_path).convert('RGB')

run_inference(image_ref, image_tgt, True)
```

## Citation

If you find this project useful, please consider citing:

```bibtex
@inproceedings{wangorient,
  title={Orient Anything V2: Unifying Orientation and Rotation Understanding},
  author={Wang, Zehan and Zhang, Ziang and Xu, Jiayang and Wang, Jialei and Pang, Tianyu and Du, Chao and Zhao, Hengshuang and Zhao, Zhou},
  booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems}
}
```