3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection (2204.06272v1)

Abstract: 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.

An Overview of 3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection

This paper, titled "3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection", addresses the challenge of 3D visual grounding, which involves locating target objects in 3D point cloud scenes based on natural language descriptions. Traditional approaches tend to rely on a two-stage paradigm involving separate language-irrelevant detection and cross-modal matching stages. However, the authors identify significant limitations in this methodology, noting that the inherent properties of 3D point clouds (such as their irregularity and large scale) complicate the effectiveness of both sparse and dense proposals in detecting and matching the target objects.

Methodology

The proposed solution, 3D-SPS (3D Single-Stage Referred Point Progressive Selection), aims to bridge the gap between detection and matching by implementing a single-stage approach. The core idea involves progressively selecting keypoints under language guidance throughout the entire process to directly locate the target. The technique is divided into two main modules:

1. Description-aware Keypoint Sampling (DKS) Module:
   • This module coarsely focuses on keypoints associated with language-relevant objects.
   • By using object confidence scores and description relevance scores, the DKS module samples keypoints that are pertinent to the given description.
2. Target-oriented Progressive Mining (TPM) Module:
   • This module refines the selection to pinpoint the target accurately.
   • It leverages a multi-layer approach combining intra-modal relationship modeling and inter-modal target mining to progressively narrow down the keypoints.

The experimental results show that 3D-SPS achieves state-of-the-art performance across key datasets, including ScanRefer and Nr3D/Sr3D.

Experimental Results

The experimental results substantiate the efficacy of the proposed method. In the ScanRefer dataset, 3D-SPS achieves notable improvements with an [email protected] of 36.43% and [email protected] of 47.65% in the 3D only setting, surpassing prior state-of-the-art methods by significant margins. Similarly, in the Nr3D and Sr3D subsets of the ReferIt3D dataset, 3D-SPS consistently outperforms other leading methods, demonstrating the robustness of progressive keypoint selection.

Implications and Future Directions

The implications of this research extend across practical applications in autonomous robotics, augmented and virtual reality, and human-machine interaction. By enhancing the accuracy and efficiency of 3D visual grounding systems, the findings promise to facilitate more sophisticated and intuitive interactions in these domains. Additionally, the single-stage approach introduced by 3D-SPS presents a foundational shift that could inspire more cohesive and integrated methodologies in future research.

Potential Limitations

Despite its advantages, the paper acknowledges certain limitations inherent to the 3D-SPS model, particularly when dealing with complex, view-dependent descriptions and ambiguous queries. These challenges highlight areas for future exploration, aiming to refine the model's robustness against such constraints.

Conclusion

Overall, "3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection" presents a compelling advancement in the field of 3D visual grounding. By shifting from a two-stage to a single-stage process and emphasizing progressive keypoint selection under the guidance of language, the authors effectively address key challenges posed by the irregular and large-scale nature of 3D point clouds. The substantial improvements in performance metrics underscore the potential of this approach to redefine standards and inspire continued innovation in machine perception and interaction.