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We introduce the task of localizing a flexible number of objects inreal-world 3D scenes using natural language descriptions. Existing 3D visualgrounding tasks focus on localizing a unique object given a text description.However, such a strict setting is unnatural as localizing potentially multipleobjects is a common need in real-world scenarios and robotic tasks (e.g.,visual navigation and object rearrangement). To address this setting we proposeMulti3DRefer, generalizing the ScanRefer dataset and task. Our dataset contains61926 descriptions of 11609 objects, where zero, single or multiple targetobjects are referenced by each description. We also introduce a new evaluationmetric and benchmark methods from prior work to enable further investigation ofmulti-modal 3D scene understanding. Furthermore, we develop a better baselineleveraging 2D features from CLIP by rendering object proposals online withcontrastive learning, which outperforms the state of the art on the ScanReferbenchmark.

Multi3DRefer: Grounding Text Description to Multiple 3D Objects