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Understanding complex scenes at multiple levels of abstraction remains a formidable challenge in computer vision.
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Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Proc. ICCV (2021)
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Mascaro, R., Teixeira, L., Chli, M.: Diffuser: Multi-view 2D-to-3D label diffusion for semantic scene segmentation. In: Proc. ICRA (2021)
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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: Proc. ICML. vol. 139, pp. 8748–8763 (2021)
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Ha, H., Song, S.: Semantic abstraction: Open-world 3d scene understanding from 2d vision-language models. In: 6th Annual Conference on Robot Learning (2022)
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Li, B., Weinberger, K.Q., Belongie, S., Koltun, V., Ranftl, R.: Language-driven semantic segmentation. In: ICLR (2022), https://openreview.net/forum?id=RriDjddCLN
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Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. In: Proc. SIGGRAPH (2022)
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Patel, N., Flanigan, J.: Forming trees with treeformers. arXiv preprint arXiv:2207.06960 (2022)
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Sun, C., Sun, M., Chen, H.: Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In: Proc. CVPR (2022)
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Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al.: Segment anything. In: ICCV (2023)
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Liang, F., Wu, B., Dai, X., Li, K., Zhao, Y., Zhang, H., Zhang, P., Vajda, P., Marculescu, D.: Open-vocabulary semantic segmentation with mask-adapted clip. In: CVPR. pp. 7061–7070 (2023)
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2022
Cited alongside, same era.
Thrush, T., Jiang, R., Bartolo, M., Singh, A., Williams, A., Kiela, D., Ross, C.: Winoground: Probing vision and language models for visio-linguistic compositionality. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5238–5248 (2022)
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Tschernezki, V., Laina, I., Larlus, D., Vedaldi, A.: Neural Feature Fusion Fields: 3D distillation of self-supervised 2D image representation. In: Proceedings of the International Conference on 3D Vision (3DV) (2022)
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Yuksekgonul, M., Bianchi, F., Kalluri, P., Jurafsky, D., Zou, J.: When and why vision-language models behave like bags-of-words, and what to do about it? In: The Eleventh International Conference on Learning Representations (2022)
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2022
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Zhou, X., Girdhar, R., Joulin, A., Krähenbühl, P., Misra, I.: Detecting twenty-thousand classes using image-level supervision. In: ECCV (2022)
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Bhalgat, Y.S., Laina, I., Henriques, J.F., Vedaldi, A., Zisserman, A.: Contrastive Lift: 3D object instance segmentation by slow-fast contrastive fusion. In: Proceedings of Advances in Neural Information Processing Systems (NeurIPS) (2023)
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2023
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2023
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Liu, Y., Hu, B., Huang, J., Tai, Y.W., Tang, C.K.: Instance neural radiance field. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 787–796 (2023)
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Peng, S., Genova, K., Jiang, C., Tagliasacchi, A., Pollefeys, M., Funkhouser, T., et al.: Openscene: 3d scene understanding with open vocabularies. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 815–824 (2023)
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2023
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Siddiqui, Y., Porzi, L., Bulò, S.R., Müller, N., Nießner, M., Dai, A., Kontschieder, P.: Panoptic lifting for 3D scene understanding with neural fields. In: Proc. CVPR (2023)
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Tancik, M., Weber, E., Ng, E., Li, R., Yi, B., Kerr, J., Wang, T., Kristoffersen, A., Austin, J., Salahi, K., Ahuja, A., McAllister, D., Kanazawa, A.: Nerfstudio: A modular framework for neural radiance field development. In: ACM SIGGRAPH 2023 Conference Proceedings. SIGGRAPH ’23 (2023)
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Tang, J., Ren, J., Zhou, H., Liu, Z., Zeng, G.: DreamGaussian: Generative gaussian splatting for efficient 3D content creation. arXiv 2309.16653
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Wu, G., Yi, T., Fang, J., Xie, L., Zhang, X., Wei, W., Liu, W., Tian, Q., Wang, X.: 4D gaussian splatting for real-time dynamic scene rendering. arXiv (2023)
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Xu, J., Liu, S., Vahdat, A., Byeon, W., Wang, X., De Mello, S.: Open-vocabulary panoptic segmentation with text-to-image diffusion models. In: CVPR. pp. 2955–2966 (2023)
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Cen, J., Zhou, Z., Fang, J., Shen, W., Xie, L., Jiang, D., Zhang, X., Tian, Q., et al.: Segment anything in 3d with nerfs. Advances in Neural Information Processing Systems 36
2024
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Chen, H., Blomqvist, K., Milano, F., Siegwart, R.: Panoptic vision-language feature fields. IEEE Robotics and Automation Letters (2024)
2024
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2024
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2024
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Ying, H., Yin, Y., Zhang, J., Wang, F., Yu, T., Huang, R., Fang, L.: Omniseg3d: Omniversal 3d segmentation via hierarchical contrastive learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20612–20622 (2024)
2024
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