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Aiming to link natural language descriptions to specific regions in a 3Dscene represented as 3D point clouds, 3D visual grounding is a very fundamentaltask for human-robot interaction. The recognition errors can significantlyimpact the overall accuracy and then degrade the operation of AI systems.Despite their effectiveness, existing methods suffer from the difficulty of lowrecognition accuracy in cases of multiple adjacent objects with similarappearances.To address this issue, this work intuitively introduces thehuman-robot interaction as a cue to facilitate the development of 3D visualgrounding. Specifically, a new task termed Embodied Reference Understanding(ERU) is first designed for this concern. Then a new dataset called ScanERU isconstructed to evaluate the effectiveness of this idea. Different from existingdatasets, our ScanERU is the first to cover semi-synthetic scene integrationwith textual, real-world visual, and synthetic gestural information.Additionally, this paper formulates a heuristic framework based on attentionmechanisms and human body movements to enlighten the research of ERU.Experimental results demonstrate the superiority of the proposed method,especially in the recognition of multiple identical objects. Our codes anddataset are ready to be available publicly.

ScanERU: Interactive 3D Visual Grounding based on Embodied Reference Understanding