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.

翻译：我们提出了Stanford-ORB，一个全新的真实世界三维物体逆向渲染基准。近年来逆向渲染的进展推动了其在三维内容生成领域的广泛应用，该技术正迅速从研究及商业用例扩展至消费级设备。尽管相关成果持续改进，但目前尚缺乏能够定量评估与比较各类逆向渲染方法的真实世界基准。现有真实世界数据集通常仅包含物体的形状与多视角图像，不足以评估材质恢复与物体重光照的质量。具备材质与光照恢复能力的方法常依赖合成数据进行定量评估，但这无法保证其在复杂真实环境中的泛化能力。我们提出一个全新数据集，包含在多种自然场景下捕获的真实世界物体，并配有真值三维扫描、多视角图像及环境光照。基于该数据集，我们建立了首个针对开放场景中物体逆向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。