MASSTAR: A Multi-Modal and Large-Scale Scene Dataset with a Versatile Toolchain for Surface Prediction and Completion
arxiv(2024)
摘要
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.
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