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A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directions

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arxiv 2406.05785 v2 pith:4YOYD5JF submitted 2024-06-09 cs.CV

classification cs.CV
keywords t-3dvgresearchdirectionsgroundingsurveyvisualadvanceselements
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Text-guided 3D visual grounding (T-3DVG), which aims to locate a specific object that semantically corresponds to a language query from a complicated 3D scene, has drawn increasing attention in the 3D research community over the past few years. Compared to 2D visual grounding, this task presents great potential and challenges due to its closer proximity to the real world and the complexity of data collection and 3D point cloud source processing. In this survey, we attempt to provide a comprehensive overview of the T-3DVG progress, including its fundamental elements, recent research advances, and future research directions. To the best of our knowledge, this is the first systematic survey on the T-3DVG task. Specifically, we first provide a general structure of the T-3DVG pipeline with detailed components in a tutorial style, presenting a complete background overview. Then, we summarize the existing T-3DVG approaches into different categories and analyze their strengths and weaknesses. We also present the benchmark datasets and evaluation metrics to assess their performances. Finally, we discuss the potential limitations of existing T-3DVG and share some insights on several promising research directions. The latest papers are continually collected at https://github.com/liudaizong/Awesome-3D-Visual-Grounding.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Visual Grounding from Event Cameras

    cs.CV 2025-09 conditional novelty 7.0 of 10

    Talk2Event provides 5,567 event-camera driving scenes, 13,458 objects, and 30,690 human-validated referring expressions labeled with appearance, status, relation-to-viewer, and relation-to-others attributes.

  2. SPAZER: Spatial-Semantic Progressive Reasoning Agent for Zero-shot 3D Visual Grounding

    cs.CV 2025-06 conditional novelty 6.0 of 10

    SPAZER, a VLM-driven agent, combines 3D rendered views with 2D camera images in a progressive pipeline to achieve state-of-the-art zero-shot 3D visual grounding.

  3. Fast3D: Accelerating 3D Multi-modal Large Language Models for Efficient 3D Scene Understanding

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Fast3D prunes up to 90% of object-centric visual tokens in 3D MLLMs while preserving about 96.8% of original benchmark performance, using a trained attention predictor and adaptive layer-wise pruning.

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