  3D visual grounding aims to locate the target object mentioned by free-formed
natural language descriptions in 3D point cloud scenes. Most previous work
requires the encoder-decoder to simultaneously align the attribute information
of the target object and its relational information with the surrounding
environment across modalities. This causes the queries' attention to be
dispersed, potentially leading to an excessive focus on points irrelevant to
the input language descriptions. To alleviate these issues, we propose PD-TPE,
a visual-language model with a double-branch decoder. The two branches perform
proposal feature decoding and surrounding layout awareness in parallel. Since
their attention maps are not influenced by each other, the queries focus on
tokens relevant to each branch's specific objective. In particular, we design a
novel Text-guided Position Encoding method, which differs between the two
branches. In the main branch, the priori relies on the relative positions
between tokens and predicted 3D boxes, which direct the model to pay more
attention to tokens near the object; in the surrounding branch, it is guided by
the similarity between visual and text features, so that the queries attend to
tokens that can provide effective layout information. Extensive experiments
demonstrate that we surpass the state-of-the-art on two widely adopted 3D
visual grounding datasets, ScanRefer and NR3D, by 1.8% and 2.2%, respectively.
Codes will be made publicly available.
