  Grounding referring expressions in RGBD image has been an emerging field. We
present a novel task of 3D visual grounding in single-view RGBD image where the
referred objects are often only partially scanned due to occlusion. In contrast
to previous works that directly generate object proposals for grounding in the
3D scenes, we propose a bottom-up approach to gradually aggregate context-aware
information, effectively addressing the challenge posed by the partial
geometry. Our approach first fuses the language and the visual features at the
bottom level to generate a heatmap that coarsely localizes the relevant regions
in the RGBD image. Then our approach conducts an adaptive feature learning
based on the heatmap and performs the object-level matching with another
visio-linguistic fusion to finally ground the referred object. We evaluate the
proposed method by comparing to the state-of-the-art methods on both the RGBD
images extracted from the ScanRefer dataset and our newly collected SUNRefer
dataset. Experiments show that our method outperforms the previous methods by a
large margin (by 11.2% and 15.6%<EMAIL_ADDRESS>on both datasets.
