LanguageRefer: Spatial-Language Model for 3D Visual Grounding
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Abstract (arXiv)
For robots to understand human instructions and perform meaningful tasks in the near future, it is important to develop learned models that comprehend referential language to identify common objects in real-world 3D scenes. In this paper, we introduce a spatial-language model for a 3D visual grounding problem. Specifically, given a reconstructed 3D scene in the form of point clouds with 3D bounding boxes of potential object candidates, and a language utterance referring to a target object in the scene, our model successfully identifies the target object from a set of potential candidates. Specifically, LanguageRefer uses a transformer-based architecture that combines spatial embedding from bounding boxes with fine-tuned language embeddings from DistilBert to predict the target object. We show that it performs competitively on visio-linguistic datasets proposed by ReferIt3D. Further, we analyze its spatial reasoning task performance decoupled from perception noise, the accuracy of view-dependent utterances, and viewpoint annotations for potential robotics applications.
Key facts. Cited by 88 papers in the map (data through 7 Oct 2026). Builds on 40 of its 52 references that are in the map. First posted on arXiv 7 Jul 2021.
Builds on
40 of 52 references.
References · 40
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28 more
- 2017AI2-THOR: An Interactive 3D Environment for Visual AIcited by 850
- 2021MDETR -- Modulated Detection for End-to-End Multi-Modal Understandingcited by 811
- 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 773
- 2018On Evaluation of Embodied Navigation Agentscited by 526
- 2019CLEVRER: CoLlision Events for Video REpresentation and Reasoningcited by 475
- 2019ScanRefer: 3D Object Localization in RGB-D Scans using Natural Languagecited by 446
- 2020Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environmentscited by 429
- 2018MAttNet: Modular Attention Network for Referring Expression Comprehensioncited by 427
- 2020Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Groundingcited by 401
- 2018Speaker-Follower Models for Vision-and-Language Navigationcited by 337
- 2017Embodied Question Answeringcited by 335
- 2019REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environmentscited by 330
- 2020A Recurrent Vision-and-Language BERT for Navigationcited by 300
- 2019Cross-Modal Self-Attention Network for Referring Image Segmentationcited by 263
- 2019Self-Monitoring Navigation Agent via Auxiliary Progress Estimationcited by 173
- 2021InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referringcited by 140
- 2019CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoningcited by 137
- 2021SAT: 2D Semantics Assisted Training for 3D Visual Groundingcited by 124
- 2020Topological Planning with Transformers for Vision-and-Language Navigationcited by 115
- 2019Dynamic Graph Attention for Referring Expression Comprehensioncited by 111
- 2020Sim-to-Real Transfer for Vision-and-Language Navigationcited by 88
- 2019Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigationcited by 81
- 2021Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloudcited by 81
- 2019Learning Cross-modal Context Graph for Visual Groundingcited by 49
- 2021Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigationcited by 47
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Citing papers per year: 2021: 5; 2022: 14; 2023: 23; 2024: 22; 2025: 15; 2026: 9.
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