Surface prediction and completion have been widely studied in various applications. Recently, research in surface completion has evolved from small objects to complex large-scale scenes. As a result, researchers have begun increasing the volume of data and leveraging a greater variety of data modalities including rendered RGB images, descriptive texts, depth images, etc, to enhance algorithm performance. However, existing datasets suffer from a deficiency in the amounts of scene-level models along with the corresponding multi-modal information. Therefore, a method to scale the datasets and generate multi-modal information in them efficiently is essential. To bridge this research gap, we propose MASSTAR: a Multi-modal lArge-scale Scene dataset with a verSatile Toolchain for surfAce pRediction and completion. We develop a versatile and efficient toolchain for processing the raw 3D data from the environments. It screens out a set of fine-grained scene models and generates the corresponding multi-modal data. Utilizing the toolchain, we then generate an example dataset composed of over a thousand scene-level models with partial real-world data added. We compare MASSTAR with the existing datasets, which validates its superiority: the ability to efficiently extract high-quality models from complex scenarios to expand the dataset. Additionally, several representative surface completion algorithms are benchmarked on MASSTAR, which reveals that existing algorithms can hardly deal with scene-level completion. We will release the source code of our toolchain and the dataset. For more details, please see our project page at https://sysu-star.github.io/MASSTAR.

翻译：表面预测与补全已在多种应用中被广泛研究。近年来，表面补全的研究已从小物体扩展到复杂的大规模场景。为此，研究者开始增加数据规模并利用更多种类的数据模态（包括渲染RGB图像、描述性文本、深度图像等）来提升算法性能。然而，现有数据集在场景级模型数量及其对应的多模态信息方面存在不足。因此，亟需一种能够高效扩展数据集并生成其中多模态信息的方法。为填补这一研究空白，我们提出了MASSTAR：一个面向表面预测与补全的多模态大规模场景数据集与通用工具链。我们开发了一套通用且高效的工具链，用于处理环境中的原始3D数据。该工具链能够筛选出一组精细的场景模型，并生成相应的多模态数据。利用该工具链，我们生成了一个包含上千个场景级模型并融合部分真实世界数据的示例数据集。我们将MASSTAR与现有数据集进行对比，验证了其优越性——能够从复杂场景中高效提取高质量模型以扩展数据集。此外，我们在MASSTAR上对多种代表性表面补全算法进行了基准测试，结果表明现有算法难以应对场景级补全任务。我们将公开工具链的源代码及数据集。更多详情请参见项目页面：https://sysu-star.github.io/MASSTAR。