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Locating objects described in natural language presents a significant challenge for autonomous agents.
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 5828–5839
2017
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I. Armeni, Z.-Y. He, J. Gwak, A. R. Zamir, M. Fischer, J. Malik, and S. Savarese, “3d scene graph: A structure for unified semantics, 3d space, and camera,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 5664–5673
2019
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U.-H. Kim, J.-M. Park, T.-J. Song, and J.-H. Kim, “3-d scene graph: A sparse and semantic representation of physical environments for intelligent agents,” IEEE transactions on cybernetics , vol. 50, no. 12, pp. 4921–4933, 2019
2019
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2019
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J. Wald, H. Dhamo, N. Navab, and F. Tombari, “Learning 3d semantic scene graphs from 3d indoor reconstructions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 3961–3970
2020
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D. Z. Chen, A. X. Chang, and M. Nießner, “Scanrefer: 3d object localization in rgb-d scans using natural language,” in European conference on computer vision . Springer, 2020, pp. 202–221
2020
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P. Achlioptas, A. Abdelreheem, F. Xia, M. Elhoseiny, and L. J. Guibas, “ReferIt3D: Neural listeners for fine-grained 3d object identification in real-world scenes,” in 16th European Conference on Computer Vision (ECCV) , 2020
2020
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A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
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M. Tsimpoukelli, J. L. Menick, S. Cabi, S. Eslami, O. Vinyals, and F. Hill, “Multimodal few-shot learning with frozen language models,” Advances in Neural Information Processing Systems , vol. 34, pp. 200–212, 2021
2021
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2021
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L. Zhao, D. Cai, L. Sheng, and D. Xu, “3dvg-transformer: Relation modeling for visual grounding on point clouds,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 2928–2937
2021
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C. Campos, R. Elvira, J. J. G. Rodríguez, J. M. Montiel, and J. D. Tardós, “Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam,” IEEE Transactions on Robotics , vol. 37, no. 6, pp. 1874–1890, 2021
2021
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N. Mu, A. Kirillov, D. Wagner, and S. Xie, “Slip: Self-supervision meets language-image pre-training,” in European conference on computer vision . Springer, 2022, pp. 529–544
2022
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2022
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J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou, et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022
2022
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2022
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2022
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J. Wald, N. Navab, and F. Tombari, “Learning 3d semantic scene graphs with instance embeddings,” International Journal of Computer Vision , vol. 130, no. 3, pp. 630–651, 2022
2022
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S. Chen, P.-L. Guhur, M. Tapaswi, C. Schmid, and I. Laptev, “Language conditioned spatial relation reasoning for 3d object grounding,” Advances in neural information processing systems , vol. 35, pp. 20 522–20 535, 2022
2022
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J. Li, P. Wang, P. Xiong, T. Cai, Z. Yan, L. Yang, J. Liu, H. Fan, and S. Liu, “Practical stereo matching via cascaded recurrent network with adaptive correlation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 16 263–16 272
2022
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2023
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X. Zhai, B. Mustafa, A. Kolesnikov, and L. Beyer, “Sigmoid loss for language image pre-training,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 11 975–11 986
2023
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2023
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al. , “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4015–4026
2023
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H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” 2023
2023
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R. Girdhar, A. El-Nouby, Z. Liu, M. Singh, K. V. Alwala, A. Joulin, and I. Misra, “Imagebind: One embedding space to bind them all,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 15 180–15 190
2023
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J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” in International conference on machine learning . PMLR, 2023, pp. 19 730–19 742
2023
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2023
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S.-C. Wu, K. Tateno, N. Navab, and F. Tombari, “Incremental 3d semantic scene graph prediction from rgb sequences,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5064–5074
2023
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H. Liu, C. Li, Y. Li, and Y. J. Lee, “Improved baselines with visual instruction tuning,” 2023
2023
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K. Kim, K. Yoon, J. Jeon, Y. In, J. Moon, D. Kim, and C. Park, “Llm4sgg: Large language model for weakly supervised scene graph generation,” arXiv e-prints , pp. arXiv–2310, 2023
2023
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Y. Hong, H. Zhen, P. Chen, S. Zheng, Y. Du, Z. Chen, and C. Gan, “3d-llm: Injecting the 3d world into large language models,” Advances in Neural Information Processing Systems , vol. 36, pp. 20 482–20 494, 2023
2023
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2023
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2024
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X. Zou, J. Yang, H. Zhang, F. Li, L. Li, J. Wang, L. Wang, J. Gao, and Y. J. Lee, “Segment everything everywhere all at once,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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H. Liu, C. Li, Y. Li, B. Li, Y. Zhang, S. Shen, and Y. J. Lee, “Llava-next: Improved reasoning, ocr, and world knowledge,” January 2024. [Online]. Available: https://llava-vl.github.io/blog/2024-01-30-llava-next/
2024
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
K. Jatavallabhula, A. Kuwajerwala, Q. Gu, M. Omama, T. Chen, S. Li, G. Iyer, S. Saryazdi, N. Keetha, A. Tewari, J. Tenenbaum, C. de Melo, M. Krishna, L. Paull, F. Shkurti, and A. Torralba, “Conceptfusion: Open-set multimodal 3d mapping,” Robotics: Science and Systems (RSS) , 2023
2023
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S. Lu, H. Chang, E. P. Jing, A. Boularias, and K. Bekris, “Ovir-3d: Open-vocabulary 3d instance retrieval without training on 3d data,” in Conference on Robot Learning . PMLR, 2023, pp. 1610–1620
2023
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2023
Cited alongside, same era.
2023
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
A. Werby, C. Huang, M. Büchner, A. Valada, and W. Burgard, “Hierarchical open-vocabulary 3d scene graphs for language-grounded robot navigation,” in First Workshop on Vision-Language Models for Navigation and Manipulation at ICRA 2024 , 2024
2024
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Q. Gu, A. Kuwajerwala, S. Morin, K. M. Jatavallabhula, B. Sen, A. Agarwal, C. Rivera, W. Paul, K. Ellis, R. Chellappa, et al. , “Conceptgraphs: Open-vocabulary 3d scene graphs for perception and planning,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024
2024
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2024
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S. Huang, L. Dong, W. Wang, Y. Hao, S. Singhal, S. Ma, T. Lv, L. Cui, O. K. Mohammed, B. Patra, et al. , “Language is not all you need: Aligning perception with language models,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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2024
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C. Zhang, X. Yang, J. Hou, K. Kitani, W. Cai, and F.-J. Chu, “Egosg: Learning 3d scene graphs from egocentric rgb-d sequences,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 2535–2545
2024
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L. Chen, X. Wang, J. Lu, S. Lin, C. Wang, and G. He, “Clip-driven open-vocabulary 3d scene graph generation via cross-modality contrastive learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 27 863–27 873
2024
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D. Honerkamp, M. Büchner, F. Despinoy, T. Welschehold, and A. Valada, “Language-grounded dynamic scene graphs for interactive object search with mobile manipulation,” IEEE Robotics and Automation Letters , 2024
2024
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A. Majumdar, A. Ajay, X. Zhang, P. Putta, S. Yenamandra, M. Henaff, S. Silwal, P. Mcvay, O. Maksymets, S. Arnaud, et al. , “Openeqa: Embodied question answering in the era of foundation models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 16 488–16 498
2024
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Z. Yuan, J. Ren, C.-M. Feng, H. Zhao, S. Cui, and Z. Li, “Visual programming for zero-shot open-vocabulary 3d visual grounding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 20 623–20 633
2024
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J. Yang, X. Chen, S. Qian, N. Madaan, M. Iyengar, D. F. Fouhey, and J. Chai, “Llm-grounder: Open-vocabulary 3d visual grounding with large language model as an agent,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 7694–7701
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T. Miyanishi, D. Azuma, S. Kurita, and M. Kawanabe, “Cross3dvg: Cross-dataset 3d visual grounding on different rgb-d scans,” in 2024 International Conference on 3D Vision (3DV) . IEEE, 2024, pp. 717–727
2024
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L. Yang, Y. Wang, X. Li, X. Wang, and J. Yang, “Fine-grained visual prompting,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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AI@Meta, “Llama 3 model card,” 2024. [Online]. Available: https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md
2024
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N. Karaev, I. Rocco, B. Graham, N. Neverova, A. Vedaldi, and C. Rupprecht, “Cotracker: It is better to track together,” in European Conference on Computer Vision . Springer, 2024, pp. 18–35
2024
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M. Muzammul and X. Li, “Comprehensive review of deep learning-based tiny object detection: challenges, strategies, and future directions,” Knowledge and Information Systems , pp. 1–89, 2025
2025
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