Orient Anything V2: Unifying Orientation and Rotation Understanding

2026-01-09

arXiv.org

AI Summary

This work proposes a unified framework for jointly inferring 3D object orientation and relative rotation from either single or paired images, with particular effectiveness on objects exhibiting complex rotational symmetries. The approach integrates a multi-frame architecture with a symmetry-aware periodic distribution regression objective and introduces a scalable generative 3D asset pipeline coupled with a closed-loop model annotation system to enable efficient self-supervised training. The resulting model achieves state-of-the-art performance across eleven established benchmarks in zero-shot 3D orientation estimation, 6DoF pose prediction, and symmetry recognition, significantly enhancing generalization capabilities for downstream tasks.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This work presents Orient Anything V2, an enhanced foundation model for unified understanding of object 3D orientation and rotation from single or paired images. Building upon Orient Anything V1, which defines orientation via a single unique front face, V2 extends this capability to handle objects with diverse rotational symmetries and directly estimate relative rotations. These improvements are enabled by four key innovations: 1) Scalable 3D assets synthesized by generative models, ensuring broad category coverage and balanced data distribution; 2) An efficient, model-in-the-loop annotation system that robustly identifies 0 to N valid front faces for each object; 3) A symmetry-aware, periodic distribution fitting objective that captures all plausible front-facing orientations, effectively modeling object rotational symmetry; 4) A multi-frame architecture that directly predicts relative object rotations. Extensive experiments show that Orient Anything V2 achieves state-of-the-art zero-shot performance on orientation estimation, 6DoF pose estimation, and object symmetry recognition across 11 widely used benchmarks. The model demonstrates strong generalization, significantly broadening the applicability of orientation estimation in diverse downstream tasks.

Problem

• 3D orientation
• rotation estimation
• rotational symmetry
• relative rotation
• object pose

Innovation

• rotational symmetry
• foundation model
• relative rotation estimation
• model-in-the-loop annotation
• periodic distribution fitting