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

TL;DR

The paper tackles the scarcity of object orientation annotations by mining 3D assets to synthesize a large-scale, precisely labeled dataset of object orientations. It introduces Orient Anything, a ViT-based model that predicts orientation as probability distributions over polar, azimuth, and rotation angles, with a front-face confidence head to handle symmetry. A dedicated synthetic-to-real transfer strategy—combining real-world pretraining (DINOv2) and domain-gap data augmentation—enables strong zero-shot performance on real images, surpassing Cube RCNN and large VLM baselines. The approach yields state-of-the-art orientation estimation on both rendered and real data and unlocks applications in spatial understanding, generation scoring, and 3D orientation voting for downstream tasks.

Abstract

Orientation is a key attribute of objects, crucial for understanding their spatial pose and arrangement in images. However, practical solutions for accurate orientation estimation from a single image remain underexplored. In this work, we introduce Orient Anything, the first expert and foundational model designed to estimate object orientation in a single- and free-view image. Due to the scarcity of labeled data, we propose extracting knowledge from the 3D world. By developing a pipeline to annotate the front face of 3D objects and render images from random views, we collect 2M images with precise orientation annotations. To fully leverage the dataset, we design a robust training objective that models the 3D orientation as probability distributions of three angles and predicts the object orientation by fitting these distributions. Besides, we employ several strategies to improve synthetic-to-real transfer. Our model achieves state-of-the-art orientation estimation accuracy in both rendered and real images and exhibits impressive zero-shot ability in various scenarios. More importantly, our model enhances many applications, such as comprehension and generation of complex spatial concepts and 3D object pose adjustment.

Figures (14)

• Figure 1: Understanding object orientation is essential for spatial reasoning. However, even advanced VLMs like GPT-4o and Gem- ini-1.5-pro are not yet able to resolve the basic orientation issue.
• Figure 2: The orientation data collection pipeline is composed of three steps: 1) Canonical 3D Model Filtering: This step removes any 3D objects in tilted poses. 2) Orientation Annotating: An advanced 2D VLM is used to identify the front face from multiple orthogonal perspectives, with view symmetry employed to narrow the potential choices. 3) Free-view Rendering: Rendering images from random and free viewpoints, and the object orientation is represented by the polar $\theta$, azimuthal $\varphi$ and rotation angle $\delta$ of the camera.
• Figure 3: Orient Anything consists of a simple visual encoder and multiple prediction heads. It is trained to judge if the object in the input image has a meaningful front face and fits the probability distribution of 3D orientation.
• Figure 4: Ablation study for hyper-parameter $\sigma_\theta$, $\sigma_\varphi$ and $\sigma_\delta$.
• Figure 5: Generated images with given textual prompt (left two from DALL-E 3 betker2023improving, right two from FLUX flux). Accurate orientation estimation is helpful to confirm whether generated contents follow the given orientation or perspective condition.