MASSTAR: A Multi-Modal and Large-Scale Scene Dataset with a Versatile Toolchain for Surface Prediction and Completion

• URL: http://arxiv.org/abs/2403.11681v1
• Date: Mon, 18 Mar 2024 11:35:18 GMT
• Title: MASSTAR: A Multi-Modal and Large-Scale Scene Dataset with a Versatile Toolchain for Surface Prediction and Completion
• Authors: Guiyong Zheng, Jinqi Jiang, Chen Feng, Shaojie Shen, Boyu Zhou,
• Abstract summary: MASSTAR is 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. We generate an example dataset composed of over a thousand scene-level models with partial real-world data.
• Score: 25.44529512862336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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