  Compared with the visual grounding in 2D images, the natural-language-guided
3D object localization on point clouds is more challenging due to the sparse
and disordered property. In this paper, we propose a new model, named
InstanceRefer, to achieve a superior 3D visual grounding through unifying
instance attribute, relation and localization perceptions. In practice, based
on the predicted target category from natural language, our model first filters
instances from panoptic segmentation on point clouds to obtain a small number
of candidates. Note that such instance-level candidates are more effective and
rational than the redundant 3D object-proposal candidates. Then, for each
candidate, we conduct the cooperative holistic scene-language understanding,
i.e., multi-level contextual referring from instance attribute perception,
instance-to-instance relation perception and instance-to-background global
localization perception. Eventually, the most relevant candidate is localized
effectively through adaptive confidence fusion. Experiments confirm that our
InstanceRefer outperforms previous state-of-the-art methods by a large margin,
i.e., 9.5% improvement on the ScanRefer benchmark (ranked 1st place) and 7.2%
improvement on Sr3D.
