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Stanford-ORB，这是一个新的真实世界三维物体逆渲染基准。近年来，逆渲染领域的进展已使多种三维内容生成应用在现实世界中成为可能，并迅速从研究和商业用途扩展至消费级设备。尽管相关成果持续提升，但目前仍缺乏能够定量评估和比较不同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的形状与多视角图像，不足以评估材质恢复和物体重光照质量。具备材质与光照恢复能力的方法往往依赖合成数据进行定量评估，但这无法保证其在复杂真实环境中的泛化能力。我们提出了一个在多种自然场景下采集的新数据集，包含真实物体的三维扫描真值、多视角图像及环境光照信息。利用该数据集，我们建立了首个针对自然场景下物体逆渲染任务的综合真实世界评估基准，并对现有多种方法的性能进行了比较。