3DRP-Net: 3D Relative Position-aware Network for 3D Visual Grounding
Zehan Wang Haifeng Huang Yang Zhao Linjun Li Xize Cheng Yichen Zhu Aoxiong Yin Zhou Zhao
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.