【第131期】Orient Anything：一种用于估计图像中物体方向的模型

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

Summary

The paper introduces Orient Anything, a novel model for estimating object orientation in images. It addresses the challenge of limited labeled data by generating a large dataset of rendered 3D models with precise orientation annotations. The model uses a probability distribution fitting approach for robust orientation prediction, improving accuracy on both rendered and real images. Furthermore, the research demonstrates Orient Anything's superior performance compared to existing methods and its potential applications in spatial reasoning and image generation. Ablation studies validate key design choices, showcasing the model's effectiveness and robustness.

这篇论文介绍了Orient Anything，一种用于估计图像中物体方向的新型模型。该模型解决了有限标注数据的问题，通过生成大量渲染的 3D 模型，并提供精确的方向注释来扩充数据集。模型采用概率分布拟合方法进行稳健的方向预测，提高了在渲染图像和真实图像上的准确性。此外，研究表明，Orient Anything 在性能上优于现有方法，并展示了它在空间推理和图像生成等应用中的潜力。消融实验验证了关键设计选择，展示了该模型的有效性和鲁棒性。

原文链接：https://arxiv.org/abs/2412.18605