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Recent progress in 3D scene understanding has explored visual grounding(3DVG) to localize a target object through a language description. However,existing methods only consider the dependency between the entire sentence andthe target object, ignoring fine-grained relationships between contexts andnon-target ones. In this paper, we extend 3DVG to a more fine-grained andinterpretable task, called 3D Phrase Aware Grounding (3DPAG). The 3DPAG taskaims to localize the target objects in a 3D scene by explicitly identifying allphrase-related objects and then conducting the reasoning according tocontextual phrases. To tackle this problem, we manually labeled about 227Kphrase-level annotations using a self-developed platform, from 88K sentences ofwidely used 3DVG datasets, i.e., Nr3D, Sr3D and ScanRefer. By tapping on ourdatasets, we can extend previous 3DVG methods to the fine-grained phrase-awarescenario. It is achieved through the proposed novel phrase-object alignmentoptimization and phrase-specific pre-training, boosting conventional 3DVGperformance as well. Extensive results confirm significant improvements, i.e.,previous state-of-the-art method achieves 3.9%, 3.5% and 4.6% overall accuracygains on Nr3D, Sr3D and ScanRefer respectively.

Toward Explainable and Fine-Grained 3D Grounding through Referring Textual Phrases