Toward Fine-Grained 3-D Visual Grounding Through Referring Textual Phrases

Recent progress in 3-D scene understanding has explored visual grounding [3D visual grounding (3DVG)] to localize a target object through a language description. However, existing methods only consider the dependency between the entire sentence and the target object, ignoring fine-grained relationships between contexts and nontarget ones. In this article, we extend 3DVG to a more fine-grained task, called 3D phrase-aware grounding (3DPAG). The 3DPAG task aims to localize the target objects in a 3-D scene by explicitly identifying all phrase-related objects and then conducting the reasoning according to contextual phrases. To tackle this problem, we manually labeled about 227 K phrase-level annotations using a self-developed platform, from 88 K sentences of widely used 3DVG datasets, i.e., Natural Reference in 3-D (Nr3D), Spatial Reference in 3-D (Sr3D), and ScanRefer. By tapping on our datasets, we can extend previous 3DVG methods to the fine-grained phrase-aware scenario. It is achieved through the proposed novel phrase-object alignment (POA) optimization and phrase-specific pretraining (PSP), boosting conventional 3DVG performance as well. Extensive results confirm significant improvements, i.e., previous state-of-the-art method achieves 3.9%, 3.5%, and 4.6% overall accuracy gains on Nr3D, Sr3D, and ScanRefer, respectively. Our datasets and platform are released in https://github.com/CurryYuan/PhraseRefer.