Datasets:
File size: 3,411 Bytes
399783d 18b4588 399783d 18b4588 |
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 |
---
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}
}
``` |