@inproceedings{477689d09d434ec2b760e2c08df34364,
title = "Bidirectional Edge-Based 3D Scene Graph Generation from Point Clouds",
abstract = "The study of 3D scene understanding is a crucial area in computer vision and has received much attention. While ignoring the relationships between objects, current research on 3D scene understanding mostly focuses on object-level knowledge. In recent years, 3D scene graph has become an effective tool for attaining greater comprehension and perception of the surroundings. However, the long-tailed distribution of the training data causes existing 3D scene graph prediction models to yield sub-optimal scene graphs. In this paper, we provide a straightforward but effective 3D scene graph prediction model. Experiments on the 3DSSG dataset demonstrate that the model described in this research when compared to the baseline model, can increase relationship prediction's effectiveness and accuracy while successfully reducing the long-tailed distribution impact brought on by dataset labeling.",
keywords = "3D scene graph, bidirectional edge, point clouds, scene understanding",
author = "Shan Yang and Ruofan Wang and Lijin Fang and Chule Yang and Yi Yang and Yufeng Yue",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Unmanned Systems, ICUS 2023 ; Conference date: 13-10-2023 Through 15-10-2023",
year = "2023",
doi = "10.1109/ICUS58632.2023.10318268",
language = "English",
series = "Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1714--1719",
editor = "Rong Song",
booktitle = "Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023",
address = "United States",
}