3D visual grounding aims to localize the target object in a 3D point cloud by
a free-form language description. Typically, the sentences describing the
target object tend to provide information about its relative relation between
other objects and its position within the whole scene. In this work, we propose
a relation-aware one-stage framework, named 3D Relative Position-aware Network
(3DRP-Net), which can effectively capture the relative spatial relationships
between objects and enhance object attributes. Specifically, 1) we propose a 3D
Relative Position Multi-head Attention (3DRP-MA) module to analyze relative
relations from different directions in the context of object pairs, which helps
the model to focus on the specific object relations mentioned in the sentence.
2) We designed a soft-labeling strategy to alleviate the spatial ambiguity
caused by redundant points, which further stabilizes and enhances the learning
process through a constant and discriminative distribution. Extensive
experiments conducted on three benchmarks (i.e., ScanRefer and Nr3D/Sr3D)
demonstrate that our method outperforms all the state-of-the-art methods in
general. The source code will be released on GitHub.

本研究提出了一种名为 3DRP-Net 的关系感知单阶段框架，能够有效捕捉物体之间的相对空间关系，并增强物体属性。通过分析对象对之间的相对关系和减少冗余点引起的空间模糊性，我们的方法在三个基准测试中均优于所有现有的方法。