  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.
