  3D visual grounding aims to locate the referred target object in 3D point
cloud scenes according to a free-form language description. Previous methods
mostly follow a two-stage paradigm, i.e., language-irrelevant detection and
cross-modal matching, which is limited by the isolated architecture. In such a
paradigm, the detector needs to sample keypoints from raw point clouds due to
the inherent properties of 3D point clouds (irregular and large-scale), to
generate the corresponding object proposal for each keypoint. However, sparse
proposals may leave out the target in detection, while dense proposals may
confuse the matching model. Moreover, the language-irrelevant detection stage
can only sample a small proportion of keypoints on the target, deteriorating
the target prediction. In this paper, we propose a 3D Single-Stage Referred
Point Progressive Selection (3D-SPS) method, which progressively selects
keypoints with the guidance of language and directly locates the target.
Specifically, we propose a Description-aware Keypoint Sampling (DKS) module to
coarsely focus on the points of language-relevant objects, which are
significant clues for grounding. Besides, we devise a Target-oriented
Progressive Mining (TPM) module to finely concentrate on the points of the
target, which is enabled by progressive intra-modal relation modeling and
inter-modal target mining. 3D-SPS bridges the gap between detection and
matching in the 3D visual grounding task, localizing the target at a single
stage. Experiments demonstrate that 3D-SPS achieves state-of-the-art
performance on both ScanRefer and Nr3D/Sr3D datasets.

Computer Vision and Pattern Recognition