3D object grounding aims to locate the most relevant target object in a raw point cloud scene based on a free-form language description. Understanding complex and diverse descriptions, and lifting them directly to a point cloud is a new and challenging topic due to the irregular and sparse nature of point clouds. There are three main challenges in 3D object grounding: to find the main focus in the complex and diverse description; to understand the point cloud scene; and to locate the target object. In this paper, we address all three challenges. Firstly, we propose a language scene graph module to capture the rich structure and long-distance phrase correlations. Secondly, we introduce a multi-level 3D proposal relation graph module to extract the object-object and object-scene co-occurrence relationships, and strengthen the visual features of the initial proposals. Lastly, we develop a description guided 3D visual graph module to encode global contexts of phrases and proposals by a nodes matching strategy. Extensive experiments on challenging benchmark datasets (ScanRefer and Nr3D) show that our algorithm outperforms existing state-of-the-art. Our code is available at https://github.com/PNXD/FFL-3DOG.
@article{arxiv.2103.16381,
title = {Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud},
author = {Mingtao Feng and Zhen Li and Qi Li and Liang Zhang and XiangDong Zhang and Guangming Zhu and Hui Zhang and Yaonan Wang and Ajmal Mian},
journal= {arXiv preprint arXiv:2103.16381},
year = {2021}
}