Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models
Zehan Wang, Ziang Zhang, Tianyu Pang, Chao Du, Hengshuang Zhao, Zhou Zhao
2024-12-30
Summary
This paper talks about Orient Anything, a new model designed to accurately estimate the orientation of objects in images, helping computers understand how objects are positioned in space.
What's the problem?
Understanding the orientation of objects in images is important for many applications, like robotics and computer vision. However, accurately determining how an object is oriented based on just a single image is challenging, especially since existing methods often require multiple images or extensive data to work well. This limitation makes it hard for AI systems to effectively interpret visual information.
What's the solution?
To solve this problem, the authors developed Orient Anything, which uses a unique approach that involves rendering 3D models of objects to create a large dataset of images with precise orientation information. They collected 2 million images with detailed annotations about how each object is oriented. The model learns to predict the orientation by treating it as a probability distribution of angles, allowing it to understand and estimate the position of objects from just one image. This method also includes strategies to improve how well the model can adapt from synthetic (computer-generated) images to real-world scenarios.
Why it matters?
This research is important because it enhances the ability of AI systems to interpret and manipulate 3D objects in various fields, such as virtual reality, augmented reality, and robotics. By improving orientation estimation, Orient Anything can lead to better performance in applications that require precise understanding of spatial relationships, making technology more effective and user-friendly.
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.