  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.
