  3D visual grounding involves finding a target object in a 3D scene that
corresponds to a given sentence query. Although many approaches have been
proposed and achieved impressive performance, they all require dense
object-sentence pair annotations in 3D point clouds, which are both
time-consuming and expensive. To address the problem that fine-grained
annotated data is difficult to obtain, we propose to leverage weakly supervised
annotations to learn the 3D visual grounding model, i.e., only coarse
scene-sentence correspondences are used to learn object-sentence links. To
accomplish this, we design a novel semantic matching model that analyzes the
semantic similarity between object proposals and sentences in a coarse-to-fine
manner. Specifically, we first extract object proposals and coarsely select the
top-K candidates based on feature and class similarity matrices. Next, we
reconstruct the masked keywords of the sentence using each candidate one by
one, and the reconstructed accuracy finely reflects the semantic similarity of
each candidate to the query. Additionally, we distill the coarse-to-fine
semantic matching knowledge into a typical two-stage 3D visual grounding model,
which reduces inference costs and improves performance by taking full advantage
of the well-studied structure of the existing architectures. We conduct
extensive experiments on ScanRefer, Nr3D, and Sr3D, which demonstrate the
effectiveness of our proposed method.
