We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark. Recent advances in inverse rendering have enabled a wide range of real-world applications in 3D content generation, moving rapidly from research and commercial use cases to consumer devices. While the results continue to improve, there is no real-world benchmark that can quantitatively assess and compare the performance of various inverse rendering methods. Existing real-world datasets typically only consist of the shape and multi-view images of objects, which are not sufficient for evaluating the quality of material recovery and object relighting. Methods capable of recovering material and lighting often resort to synthetic data for quantitative evaluation, which on the other hand does not guarantee generalization to complex real-world environments. We introduce a new dataset of real-world objects captured under a variety of natural scenes with ground-truth 3D scans, multi-view images, and environment lighting. Using this dataset, we establish the first comprehensive real-world evaluation benchmark for object inverse rendering tasks from in-the-wild scenes, and compare the performance of various existing methods. All data, code, and models can be accessed at https://stanfordorb.github.io/.

翻译：我们提出了Stanford-ORB，一种全新的真实世界三维物体逆渲染基准。近期逆渲染领域的进展已实现三维内容生成中多种真实世界应用，并正从研究与商业案例快速渗透至消费设备。尽管相关成果持续优化，当前仍缺乏能够定量评估和比较各类逆渲染方法性能的真实世界基准。现有真实世界数据集通常仅包含物体形状与多视角图像，不足以评估材质复原与物体重光照的质量。具备材质与光照复原能力的方法往往依赖合成数据进行定量评估，但这无法保证其对复杂真实环境的泛化能力。我们构建了一个包含真实物体在多种自然场景下采集的新数据集，涵盖真实三维扫描、多视角图像及环境光照。基于该数据集，我们建立了首个面向野外场景物体逆渲染任务的综合性真实世界评估基准，并比较了多种现有方法的性能。所有数据、代码及模型均可通过https://stanfordorb.github.io/获取。