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--- |
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license: cc-by-4.0 |
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task_categories: |
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- other |
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tags: |
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- 3d |
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- computer-vision |
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- orientation-estimation |
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--- |
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# Orient Anything V2 Dataset |
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[**Project Page**](https://orient-anythingv2.github.io/) | [**Paper**](https://huggingface.co/papers/2601.05573) | [**GitHub**](https://github.com/SpatialVision/Orient-Anything-V2) |
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**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. |
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## Sample Usage |
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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): |
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```python |
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import numpy as np |
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from PIL import Image |
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import torch |
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import tempfile |
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import os |
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from paths import * |
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from vision_tower import VGGT_OriAny_Ref |
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from inference import * |
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from app_utils import * |
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mark_dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16 |
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# device = 'cuda:0' |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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if os.path.exists(LOCAL_CKPT_PATH): |
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ckpt_path = LOCAL_CKPT_PATH |
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else: |
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from huggingface_hub import hf_hub_download |
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ckpt_path = hf_hub_download(repo_id="Viglong/Orient-Anything-V2", filename=HF_CKPT_PATH, repo_type="model", cache_dir='./', resume_download=True) |
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model = VGGT_OriAny_Ref(out_dim=900, dtype=mark_dtype, nopretrain=True) |
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model.load_state_dict(torch.load(ckpt_path, map_location='cpu')) |
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model.eval() |
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model = model.to(device) |
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print('Model loaded.') |
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@torch.no_grad() |
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def run_inference(pil_ref, pil_tgt=None, do_rm_bkg=True): |
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if pil_tgt is not None: |
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if do_rm_bkg: |
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pil_ref = background_preprocess(pil_ref, True) |
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pil_tgt = background_preprocess(pil_tgt, True) |
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else: |
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if do_rm_bkg: |
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pil_ref = background_preprocess(pil_ref, True) |
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try: |
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ans_dict = inf_single_case(model, pil_ref, pil_tgt) |
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except Exception as e: |
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print("Inference error:", e) |
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raise gr.Error(f"Inference failed: {str(e)}") |
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def safe_float(val, default=0.0): |
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try: |
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return float(val) |
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except: |
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return float(default) |
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az = safe_float(ans_dict.get('ref_az_pred', 0)) |
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el = safe_float(ans_dict.get('ref_el_pred', 0)) |
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ro = safe_float(ans_dict.get('ref_ro_pred', 0)) |
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alpha = int(ans_dict.get('ref_alpha_pred', 1)) |
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if pil_tgt is not None: |
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rel_az = safe_float(ans_dict.get('rel_az_pred', 0)) |
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rel_el = safe_float(ans_dict.get('rel_el_pred', 0)) |
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rel_ro = safe_float(ans_dict.get('rel_ro_pred', 0)) |
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print("Relative Pose: Azi",rel_az,"Ele",rel_el,"Rot",rel_ro) |
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image_ref_path = 'assets/examples/F35-0.jpg' |
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image_tgt_path = 'assets/examples/F35-1.jpg' # optional |
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image_ref = Image.open(image_ref_path).convert('RGB') |
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image_tgt = Image.open(image_tgt_path).convert('RGB') |
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run_inference(image_ref, image_tgt, True) |
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``` |
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## Citation |
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If you find this project useful, please consider citing: |
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```bibtex |
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@inproceedings{wangorient, |
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title={Orient Anything V2: Unifying Orientation and Rotation Understanding}, |
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author={Wang, Zehan and Zhang, Ziang and Xu, Jiayang and Wang, Jialei and Pang, Tianyu and Du, Chao and Zhao, Hengshuang and Zhao, Zhou}, |
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booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems} |
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} |
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``` |
