  Compared with the visual grounding on 2D images, the natural-language-guided
3D object localization on point clouds is more challenging. In this paper, we
propose a new model, named InstanceRefer, to achieve a superior 3D visual
grounding through the grounding-by-matching strategy. In practice, our model
first predicts the target category from the language descriptions using a
simple language classification model. Then, based on the category, our model
sifts out a small number of instance candidates (usually less than 20) from the
panoptic segmentation of point clouds. Thus, the non-trivial 3D visual
grounding task has been effectively re-formulated as a simplified
instance-matching problem, considering that instance-level candidates are more
rational than the redundant 3D object proposals. Subsequently, for each
candidate, we perform the multi-level contextual inference, i.e., referring
from instance attribute perception, instance-to-instance relation perception,
and instance-to-background global localization perception, respectively.
Eventually, the most relevant candidate is selected and localized by ranking
confidence scores, which are obtained by the cooperative holistic
visual-language feature matching. Experiments confirm that our method
outperforms previous state-of-the-arts on ScanRefer online benchmark and
Nr3D/Sr3D datasets.
