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3D object grounding aims to locate the most relevant target object in a rawpoint cloud scene based on a free-form language description. Understandingcomplex and diverse descriptions, and lifting them directly to a point cloud isa new and challenging topic due to the irregular and sparse nature of pointclouds. There are three main challenges in 3D object grounding: to find themain focus in the complex and diverse description; to understand the pointcloud scene; and to locate the target object. In this paper, we address allthree challenges. Firstly, we propose a language scene graph module to capturethe rich structure and long-distance phrase correlations. Secondly, weintroduce a multi-level 3D proposal relation graph module to extract theobject-object and object-scene co-occurrence relationships, and strengthen thevisual features of the initial proposals. Lastly, we develop a descriptionguided 3D visual graph module to encode global contexts of phrases andproposals by a nodes matching strategy. Extensive experiments on challengingbenchmark datasets (ScanRefer and Nr3D) show that our algorithm outperformsexisting state-of-the-art. Our code is available athttps://github.com/PNXD/FFL-3DOG.

Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud