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This paper introduces Scene-LLM, a 3D-visual-language model that enhances embodied agents' abilities in interactive 3D indoor environments by integrating the reasoning strengths of Large Language Models (LLMs).
2016
Earlier work this paper cites.
AI2-THOR: An Interactive 3D Environment for Visual AI. ArXiv abs/1712.05474
2017
Earlier work this paper cites.
Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Niessner, M.: Scannet: Richly-annotated 3d reconstructions of indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 652–660 (2017)
2017
Earlier work this paper cites.
Riegler, G., Osman Ulusoy, A., Geiger, A.: Octnet: Learning deep 3d representations at high resolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3577–3586 (2017)
2017
Earlier work this paper cites.
Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: O-cnn: Octree-based convolutional neural networks for 3d shape analysis. ACM Transactions On Graphics (TOG) 36
2017
Earlier work this paper cites.
Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., Reid, I., Gould, S., Van Den Hengel, A.: Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3674–3683 (2018)
2018
Earlier work this paper cites.
Puig, X., Ra, K., Boben, M., Li, J., Wang, T., Fidler, S., Torralba, A.: Virtualhome: Simulating household activities via programs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8494–8502 (2018)
2018
Earlier work this paper cites.
Liu, Z., Tang, H., Lin, Y., Han, S.: Point-voxel cnn for efficient 3d deep learning. Advances in Neural Information Processing Systems 32
2019
Earlier work this paper cites.
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: Learning 3d reconstruction in function space. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4460–4470 (2019)
2019
Earlier work this paper cites.
Qi, C.R., Litany, O., He, K., Guibas, L.J.: Deep hough voting for 3d object detection in point clouds. In: proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9277–9286 (2019)
2019
Earlier work this paper cites.
Tan, F., Feng, S., Ordonez, V.: Text2scene: Generating compositional scenes from textual descriptions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6710–6719 (2019)
2019
Earlier work this paper cites.
Yu, Z., Yu, J., Cui, Y., Tao, D., Tian, Q.: Deep modular co-attention networks for visual question answering. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 6281–6290 (2019)
2019
Earlier work this paper cites.
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)
2020
Earlier work this paper cites.
Chen, D.Z., Chang, A.X., Nießner, M.: Scanrefer: 3d object localization in rgb-d scans using natural language. In: European conference on computer vision. pp. 202–221. Springer (2020)
2020
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Jiang, L., Zhao, H., Shi, S., Liu, S., Fu, C.W., Jia, J.: Pointgroup: Dual-set point grouping for 3d instance segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and Pattern recognition. pp. 4867–4876 (2020)
2020
Earlier work this paper cites.
Krantz, J., Wijmans, E., Majumdar, A., Batra, D., Lee, S.: Beyond the nav-graph: Vision-and-language navigation in continuous environments. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVIII 16. pp. 104–120. Springer (2020)
2020
Earlier work this paper cites.
Shridhar, M., Thomason, J., Gordon, D., Bisk, Y., Han, W., Mottaghi, R., Zettlemoyer, L., Fox, D.: Alfred: A benchmark for interpreting grounded instructions for everyday tasks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10740–10749 (2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
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)
2021
Earlier work this paper cites.
Feng, M., Li, Z., Li, Q., Zhang, L., Zhang, X., Zhu, G., Zhang, H., Wang, Y., Mian, A.: Free-form description guided 3d visual graph network for object grounding in point cloud. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3722–3731 (2021)
2021
Earlier work this paper cites.
Huang, P.H., Lee, H.H., Chen, H.T., Liu, T.L.: Text-guided graph neural networks for referring 3d instance segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 1610–1618 (2021)
2021
Earlier work this paper cites.
Lei, J., Li, L., Zhou, L., Gan, Z., Berg, T.L., Bansal, M., Liu, J.: Less is more: Clipbert for video-and-language learning via sparse sampling. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 7331–7341 (2021)
2021
Earlier work this paper cites.
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM 65
2021
Earlier work this paper cites.
Pashevich, A., Schmid, C., Sun, C.: Episodic Transformer for Vision-and-Language Navigation. In: ICCV (2021)
2021
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Cited alongside, same era.
Tsimpoukelli, M., Menick, J.L., Cabi, S., Eslami, S., Vinyals, O., Hill, F.: Multimodal few-shot learning with frozen language models. Advances in Neural Information Processing Systems 34
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Zhang, Y., Chai, J.: Hierarchical task learning from language instructions with unified transformers and self-monitoring. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. pp. 4202–4213. Association for Computational Linguistics, Online (Aug 2021). https://doi.org/10.18653/v1/2021.findings-acl.368, https://aclanthology.org/2021.findings-acl.368
2023
Later among the works it cites.
Hong, Y., Lin, C., Du, Y., Chen, Z., Tenenbaum, J.B., Gan, C.: 3d concept learning and reasoning from multi-view images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9202–9212 (2023)
2023
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2023
Later among the works it cites.
Huang, C., Mees, O., Zeng, A., Burgard, W.: Visual language maps for robot navigation. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 10608–10615. IEEE (2023)
2023
Later among the works it cites.
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2021
Cited alongside, same era.
Zhao, H., Jiang, L., Jia, J., Torr, P.H., Koltun, V.: Point transformer. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 16259–16268 (2021)
2021
Cited alongside, same era.
Agia, C., Jatavallabhula, K.M., Khodeir, M., Miksik, O., Vineet, V., Mukadam, M., Paull, L., Shkurti, F.: Taskography: Evaluating robot task planning over large 3d scene graphs. In: Conference on Robot Learning. pp. 46–58. PMLR (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Alayrac, J.B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al.: Flamingo: a visual language model for few-shot learning. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
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)
2022
Cited alongside, same era.
Blukis, V., Paxton, C., Fox, D., Garg, A., Artzi, Y.: A persistent spatial semantic representation for high-level natural language instruction execution. In: Conference on Robot Learning. pp. 706–717. PMLR (2022)
2022
Cited alongside, same era.
Huang, W., Abbeel, P., Pathak, D., Mordatch, I.: Language models as zero-shot planners: Extracting actionable knowledge for embodied agents. In: International Conference on Machine Learning. pp. 9118–9147. PMLR (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2023
Later among the works it cites.
Hwang, I., Kim, H., Kim, Y.M.: Text2scene: Text-driven indoor scene stylization with part-aware details. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1890–1899 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Kerr, J., Kim, C.M., Goldberg, K., Kanazawa, A., Tancik, M.: Lerf: Language embedded radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 19729–19739 (2023)
2023
Later among the works it cites.
Kurenkov, A., Lingelbach, M., Agarwal, T., Jin, E., Li, C., Zhang, R., Fei-Fei, L., Wu, J., Savarese, S., Martın-Martın, R.: Modeling dynamic environments with scene graph memory. In: International Conference on Machine Learning. pp. 17976–17993. PMLR (2023)
2023
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2023
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2023
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2023
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Shah, D., Osiński, B., Levine, S., et al.: Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action. In: Conference on Robot Learning. pp. 492–504. PMLR (2023)
2023
Later among the works it cites.
Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D., Tremblay, J., Fox, D., Thomason, J., Garg, A.: Progprompt: Generating situated robot task plans using large language models. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 11523–11530. IEEE (2023)
2023
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Xiong, W., Liu, J., Molybog, I., Zhang, H., Bhargava, P., Hou, R., Martin, L., Rungta, R., Sankararaman, K.A., Oguz, B., Khabsa, M., Fang, H., Mehdad, Y., Narang, S., Malik, K., Fan, A., Bhosale, S., Edunov, S., Lewis, M., Wang, S., Ma, H.: Effective long-context scaling of foundation models (2023)
2023
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2023
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Zhang, Y., Gong, Z., Chang, A.X.: Multi3drefer: Grounding text description to multiple 3d objects. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 15225–15236 (2023)
2023
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2023
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