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Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models
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Builds a 2M-image orientation dataset by annotating front faces of Objaverse 3D models and rendering random views, then trains a DINOv2-based model (Orient Anything) to predict object orientation via probability distribution fitting.
Problem
Estimating object orientation (azimuth, polar, rotation) from a single open-world image is underexplored because orientation annotations are scarce, and VLMs and 3D detectors perform poorly on it.
Approach
Derive orientation labels for free by annotating the front face of canonically posed 3D assets (using VLM-based multi-view identification plus symmetry checks) and rendering random views; train with Gaussian/circular-Gaussian target distributions over angle bins instead of regression, plus DINOv2 initialization and cropping augmentation for sim-to-real transfer.
Key results
- Orient Anything ViT-L reaches 73.94% Acc@22.5° azimuth on rendered test images vs 19.94% for GPT-4o and 12.44% for Cube RCNN.
- On zero-shot real benchmarks, ViT-L yields 30.09 to 67.56 azimuth absolute error across five datasets, far below Cube RCNN's 88 to 123.
- Orient Anything+LLM reaches 51.50% overall on Ori-Bench, versus 32.50% for GPT-4o and 33.00% for Gemini-1.5-pro.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Fit angle probability distributions instead of direct angle regression (ablation) | Orient Anything · Rendered images | Azimuth Acc@22.5°: 12.00 -> 71.88 | Helped |
| Fit angle probability distributions instead of discrete angle classification (ablation) | Orient Anything · Rendered images | Azimuth Acc@22.5°: 68.75 -> 71.88 | Helped |
| Render 40 views per object instead of 10 (ablation) | Orient Anything · Rendered images | Azimuth Acc@22.5°: 67.19 -> 71.88 | Helped |
| Render 40 views per object instead of 80 (ablation) | Orient Anything · Rendered images | Azimuth Acc@22.5°: 69.12 -> 71.88 | Helped |
| Initialize with DINOv2 instead of CLIP (ablation) | Orient Anything · Real images | Recognition accuracy: 49.27 -> 69.85 | Helped |
| Initialize with DINOv2 instead of MAE (ablation) | Orient Anything · Real images | Recognition accuracy: 57.26 -> 69.85 | Helped |
| Add random cropping during training (ablation) | Orient Anything · Real images | Recognition accuracy: 69.85 -> 70.19 | Helped |
| Use segmentation masks instead of bounding boxes at inference (ablation) | Orient Anything · Real images | Recognition accuracy: 67.49 -> 69.85 | Helped |
| Provide canonical orthogonal views instead of one view for orientation annotation (ablation) | Gemini-1.5-pro · Orientation annotation | Accuracy: 44.00 -> 74.00 | Helped |
| Add symmetry information to canonical views for annotation (ablation) | GPT-4o · Orientation annotation | Accuracy: 87.00 -> 92.00 | Helped |
| Add symmetry information to canonical views for annotation (ablation) | Gemini-1.5-pro · Orientation annotation | Accuracy: 74.00 -> 86.00 | Helped |
| Vary the angle-distribution variance hyperparameters (ablation) | Orient Anything · Orientation estimation | Accuracy | No clear effect |
Surprising findings
- Direct continuous angle regression fails to converge and performs poorly, while distribution fitting substantially outperforms both regression and classification.
- GPT-4o and Gemini-1.5-pro perform near random on spatial part and relation reasoning about orientation.
Limitations
- Relies on canonically posed Objaverse assets and excludes tilted objects, and VLM annotation of front faces may hallucinate.
- Orientation understanding applications ignore depth and 3D object relationships in the LLM template pipeline.
- Performs poorly on categories where front and back are ambiguous or objects are small (e.g., keyboard, remote).
Experimental setup
Datasets: Objaverse COCO SUN RGB-D KITTI nuScenes Objectron ARKitScenes
Benchmarks: Ori-Bench rendered Objaverse test set (4,800 images) COCO orientation benchmark (1,600 samples) SUN RGB-D KITTI nuScenes Objectron ARKitScenes
Metrics: accuracy absolute error Acc@22.5° Acc@5°
Baselines: Cube RCNN GPT-4o Gemini-1.5-pro CLIP initialization MAE initialization
Builds on: DINOv2 Objaverse Cube R-CNN Grounded-SAM
Closest papers by meaning
- Orient Anything V2: Unifying Orientation and Rotation Understanding (NeurIPS 2025 · similarity 0.93)
- Orientation Matters: Making 3D Generative Models Orientation-Aligned (NeurIPS 2025 · similarity 0.91)
- Symmetry-Robust 3D Orientation Estimation (ICML 2025 · similarity 0.87)
- UniK3D: Universal Camera Monocular 3D Estimation (CVPR 2025 · similarity 0.87)
- Any6D: Model-free 6D Pose Estimation of Novel Objects (CVPR 2025 · similarity 0.86)
- NOPE: Novel Object Pose Estimation from a Single Image (CVPR 2024 · similarity 0.86)
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