MASSTAR: A Multi-Modal and Large-Scale Scene Dataset with a Versatile Toolchain for Surface Prediction and Completion
<|reference_start|>MASSTAR: A Multi-Modal and Large-Scale Scene Dataset with a Versatile Toolchain for Surface Prediction and Completion: 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.<|reference_end|>
@article{zheng2024masstar:,
    title={MASSTAR: A Multi-Modal and Large-Scale Scene Dataset with a Versatile
  Toolchain for Surface Prediction and Completion},
    author={Guiyong Zheng, Jinqi Jiang, Chen Feng, Shaojie Shen, Boyu Zhou},
    journal={arXiv preprint arXiv:2403.11681},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11681},
    primaryClass={cs.RO cs.CV}
}