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3D visual grounding is the task of localizing the object in a 3D scene which is referred by a description in natural language.
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Kazemzadeh, S., Ordonez, V., Matten, M., Berg, T.: Referitgame: Referring to objects in photographs of natural scenes. In: EMNLP (2014)
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Kong, C., Lin, D., Bansal, M., Urtasun, R., Fidler, S.: What are you talking about? text-to-image coreference. In: ICCV (2014)
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Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: EMNLP (2014)
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Plummer, B.A., Wang, L., Cervantes, C.M., Caicedo, J.C., Hockenmaier, J., Lazebnik, S.: Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In: ICCV (2015)
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Mao, J., Huang, J., Toshev, A., Camburu, O., Yuille, A.L., Murphy, K.: Generation and comprehension of unambiguous object descriptions. In: ICCV (2016)
2016
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Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: ScanNet: Richly-annotated 3D reconstructions of indoor scenes. In: ICCV (2017)
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. NIPS (2017)
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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: ECCV (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: ECCV (2020)
2020
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Goyal, A., Yang, K., Yang, D., Deng, J.: Rel3D: A minimally contrastive benchmark for grounding spatial relations in 3D. In: NIPS (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: CVPR (2020)
2020
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Prabhudesai, M., Tung, H.Y.F., Javed, S.A., Sieb, M., Harley, A.W., Fragkiadaki, K.: Embodied language grounding with 3D visual feature representations. In: CVPR (2020)
2020
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Song, K., Tan, X., Qin, T., Lu, J., Liu, T.Y.: Mpnet: Masked and permuted pre-training for language understanding. NIPS (2020)
2020
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Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al.: Transformers: State-of-the-art natural language processing. In: EMNLP (2020)
2020
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Chen, Z., Gholami, A., Nießner, M., Chang, A.X.: Scan2cap: Context-aware dense captioning in rgb-d scans. In: CVPR (2021)
Chen, D.Z., Wu, Q., Nießner, M., Chang, A.X.: D3Net: a speaker-listener architecture for semi-supervised dense captioning and visual grounding in RGB-D scans. In: ECCV (2022)
2022
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2022
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Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: CVPR (2022)
2022
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Huang, S., Chen, Y., Jia, J., Wang, L.: Multi-view transformer for 3D visual grounding. In: CVPR (2022)
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: ECCV (2022)
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2021
Cited alongside, same era.
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: ICCV (2021)
2021
Cited alongside, same era.
He, T., Shen, C., Van Den Hengel, A.: Dyco3d: Robust instance segmentation of 3d point clouds through dynamic convolution. In: CVPR (2021)
2021
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Huang, P.H., Lee, H.H., Chen, H.T., Liu, T.L.: Text-guided graph neural networks for referring 3D instance segmentation. In: AAAI (2021)
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. In: ICML (2021)
2021
Cited alongside, same era.
Yang, Z., Zhang, S., Wang, L., Luo, J.: SAT: 2D semantics assisted training for 3D visual grounding. In: ICCV (2021)
2021
Cited alongside, same era.
Yuan, Z., Yan, X., Liao, Y., Zhang, R., Wang, S., Li, Z., Cui, S.: InstanceRefer: Cooperative holistic understanding for visual grounding on point clouds through instance multi-level contextual referring. In: ICCV (2021)
2021
Cited alongside, same era.
Zhang, P., Dai, X., Yang, J., Xiao, B., Yuan, L., Zhang, L., Gao, J.: Multi-scale vision Longformer: A new vision transformer for high-resolution image encoding. In: ICCV (2021)
2021
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2022
Later among the works it cites.
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: CVPR (2022)
2022
Later among the works it cites.
Roh, J., Desingh, K., Farhadi, A., Fox, D.: LanguageRefer: Spatial-language model for 3D visual grounding. In: CoRL (2022)
2022
Later among the works it cites.
Rozenberszki, D., Litany, O., Dai, A.: Language-grounded indoor 3D semantic segmentation in the wild. In: ECCV (2022)
2022
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Vu, T., Kim, K., Luu, T.M., Nguyen, X.T., Yoo, C.D.: Softgroup for 3d instance segmentation on 3d point clouds. In: CVPR (2022)
2022
Later among the works it cites.
Wu, Y., Shi, M., Du, S., Lu, H., Cao, Z., Zhong, W.: 3D instances as 1D kernels. In: ECCV (2022)
2022
Later among the works it cites.
Guo, Z., Tang, Y., Zhang, R., Wang, D., Wang, Z., Zhao, B., Li, X.: Viewrefer: Grasp the multi-view knowledge for 3d visual grounding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 15372–15383 (2023)
2023
Closest in time.
Wu, Y., Cheng, X., Zhang, R., Cheng, Z., Zhang, J.: Eda: Explicit text-decoupling and dense alignment for 3d visual grounding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19231–19242 (2023)
2023
Closest in time.
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
Closest in time.