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

TL;DR

This paper introduces Orient Anything, a model that estimates object orientation from a single image by leveraging a large synthetic dataset derived from 3D models, achieving state-of-the-art accuracy and zero-shot capabilities.

Contribution

The work presents the first expert model for single-image object orientation estimation, utilizing a novel dataset created from 3D models and a robust training approach for improved transfer to real images.

Findings

Achieves state-of-the-art accuracy on orientation estimation tasks.

Demonstrates strong zero-shot generalization to real-world images.

Enhances applications like spatial understanding and 3D pose adjustment.

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.