Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models

[**Zehan Wang**](https://scholar.google.com/citations?user=euXK0lkAAAAJ&hl=zh-CN)1* · [**Ziang Zhang**](https://scholar.google.com/citations?hl=zh-CN&user=DptGMnYAAAAJ)1* · [**Tianyu Pang**](https://scholar.google.com/citations?hl=zh-CN&user=wYDbtFsAAAAJ)2 · [**Du Chao**](https://scholar.google.com/citations?hl=zh-CN&user=QOp7xW0AAAAJ)2 · [**Hengshuang Zhao**](https://scholar.google.com/citations?user=4uE10I0AAAAJ&hl&oi=ao)3 · [**Zhou Zhao**](https://scholar.google.com/citations?user=IIoFY90AAAAJ&hl&oi=ao)1 1Zhejiang University    2SEA AI Lab    3HKU *Equal Contribution Paper PDF Project Page
**Orient Anything**, a robust image-based object orientation estimation model. By training on 2M rendered labeled images, it achieves strong zero-shot generalization ability for images in the wild. ![teaser](assets/demo.png) ## News * **2025-05-01:** Orient Anything is accepted by ICML 2025! * **2024-12-24:** [Paper](https://arxiv.org/abs/2412.18605), [Project Page](https://orient-anything.github.io), [Code](https://github.com/SpatialVision/Orient-Anything), Models, and [Demo](https://huggingface.co/spaces/Viglong/Orient-Anything) are released. ## Pre-trained models We provide **three models** of varying scales for robust object orientation estimation in images: | Model | Params | Checkpoint | |:-|-:|:-:| | Orient-Anything-Small | 23.3 M | [Download](https://huggingface.co/Viglong/OriNet/blob/main/cropsmallEx03/dino_weight.pt) | | Orient-Anything-Base | 87.8 M | [Download](https://huggingface.co/Viglong/OriNet/blob/main/cropbaseEx032/dino_weight.pt) | | Orient-Anything-Large | 305 M | [Download](https://huggingface.co/Viglong/OriNet/blob/main/croplargeEX2/dino_weight.pt) | ## Usage ### 1 Prepraration ```bash pip install -r requirements.txt ``` ### 2 Use our models #### 2.1 In Gradio app Start gradio by executing the following script: ```bash python app.py ``` then open GUI page(default is https://127.0.0.1:7860) in web browser. or, you can try it in our [Huggingface-Space](https://huggingface.co/spaces/Viglong/Orient-Anything) #### 2.2 In Python Scripts ```python from paths import * from vision_tower import DINOv2_MLP from transformers import AutoImageProcessor import torch from PIL import Image import torch.nn.functional as F from utils import * from inference import * from huggingface_hub import hf_hub_download ckpt_path = hf_hub_download(repo_id="Viglong/Orient-Anything", filename="croplargeEX2/dino_weight.pt", repo_type="model", cache_dir='./', resume_download=True) print(ckpt_path) save_path = './' device = 'cuda' if torch.cuda.is_available() else 'cpu' dino = DINOv2_MLP( dino_mode = 'large', in_dim = 1024, out_dim = 360+180+180+2, evaluate = True, mask_dino = False, frozen_back = False ) dino.eval() print('model create') dino.load_state_dict(torch.load(ckpt_path, map_location='cpu')) dino = dino.to(device) print('weight loaded') val_preprocess = AutoImageProcessor.from_pretrained(DINO_LARGE, cache_dir='./') image_path = '/path/to/image' origin_image = Image.open(image_path).convert('RGB') angles = get_3angle(origin_image, dino, val_preprocess, device) azimuth = float(angles[0]) polar = float(angles[1]) rotation = float(angles[2]) confidence = float(angles[3]) ``` ### Best Practice To avoid ambiguity, our model only supports inputs that contain images of a single object. For daily images that usually contain multiple objects, it is a good choice to isolate each object with DINO-grounding and predict the orientation separately. ```python [ToDo] ``` ### Test-Time Augmentation In order to further enhance the robustness of the model，We further propose the test-time ensemble strategy. The input images will be randomly cropped into different variants, and the predicted orientation of different variants will be voted as the final prediction result. We implement this strategy in functions `get_3angle_infer_aug()` and `get_crop_images()`. ## Citation If you find this project useful, please consider citing: ```bibtex @article{orient_anything, title={Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models}, author={Wang, Zehan and Zhang, Ziang and Pang, Tianyu and Du, Chao and Zhao, Hengshuang and Zhao, Zhou}, journal={arXiv:2412.18605}, year={2024} } ``` ## Acknowledgement Thanks to the open source of the following projects: [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything), [render-py](https://github.com/tvytlx/render-py)