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Although great progress has been made in 3D visual grounding, current models still rely on explicit textual descriptions for grounding and lack the ability to reason human intentions from implicit instructions.
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Achlioptas, P., Abdelreheem, A., Xia, F., Elhoseiny, M., Guibas, L.: Referit3d: Neural listeners for fine-grained 3d object identification in real-world scenes. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16. pp. 422–440. Springer (2020)
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Chen, D.Z., Chang, A.X., Nießner, M.: Scanrefer: 3d object localization in rgb-d scans using natural language. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XX. pp. 202–221. Springer (2020)
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Chen, Z., Gholami, A., Nießner, M., Chang, A.X.: Scan2cap: Context-aware dense captioning in rgb-d scans. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3193–3203 (2021)
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Yang, Z., Zhang, S., Wang, L., Luo, J.: Sat: 2d semantics assisted training for 3d visual grounding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1856–1866 (2021)
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Zhao, L., Cai, D., Sheng, L., Xu, D.: 3dvg-transformer: Relation modeling for visual grounding on point clouds. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2928–2937 (2021)
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Azuma, D., Miyanishi, T., Kurita, S., Kawanabe, M.: Scanqa: 3d question answering for spatial scene understanding. In: proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 19129–19139 (2022)
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Cai, D., Zhao, L., Zhang, J., Sheng, L., Xu, D.: 3djcg: A unified framework for joint dense captioning and visual grounding on 3d point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16464–16473 (2022)
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Chen, S., Guhur, P.L., Tapaswi, M., Schmid, C., Laptev, I.: Language conditioned spatial relation reasoning for 3d object grounding. Advances in Neural Information Processing Systems 35
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Huang, S., Chen, Y., Jia, J., Wang, L.: Multi-view transformer for 3d visual grounding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15524–15533 (2022)
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Jain, A., Gkanatsios, N., Mediratta, I., Fragkiadaki, K.: Bottom up top down detection transformers for language grounding in images and point clouds. In: Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXVI. pp. 417–433. Springer (2022)
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Luo, J., Fu, J., Kong, X., Gao, C., Ren, H., Shen, H., Xia, H., Liu, S.: 3d-sps: Single-stage 3d visual grounding via referred point progressive selection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16454–16463 (2022)
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Roh, J., Desingh, K., Farhadi, A., Fox, D.: Languagerefer: Spatial-language model for 3d visual grounding. In: Conference on Robot Learning. pp. 1046–1056. PMLR (2022)
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2022
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Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., Zhou, D., et al.: Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35
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2023
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2023
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2023
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2023
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2022
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Ye, S., Chen, D., Han, S., Liao, J.: 3d question answering. IEEE Transactions on Visualization and Computer Graphics (2022)
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Yuan, Z., Yan, X., Liao, Y., Guo, Y., Li, G., Cui, S., Li, Z.: X-trans2cap: Cross-modal knowledge transfer using transformer for 3d dense captioning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8563–8573 (2022)
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2022
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2022
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2023
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2023
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2023
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OpenAI: Gpt-4 technical report (2023)
2023
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Schult, J., Engelmann, F., Hermans, A., Litany, O., Tang, S., Leibe, B.: Mask3d: Mask transformer for 3d semantic instance segmentation. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 8216–8223. IEEE (2023)
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Team, I.: Internlm: A multilingual language model with progressively enhanced capabilities (2023)
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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Zhu, Z., Ma, X., Chen, Y., Deng, Z., Huang, S., Li, Q.: 3d-vista: Pre-trained transformer for 3d vision and text alignment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2911–2921 (2023)
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Hong, Y., Zhen, H., Chen, P., Zheng, S., Du, Y., Chen, Z., Gan, C.: 3d-llm: Injecting the 3d world into large language models. Advances in Neural Information Processing Systems 36
2024
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