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Recently, methods have been proposed for 3D open-vocabulary semantic segmentation.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in
2014
Earlier work this paper cites.
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in
2017
Earlier work this paper cites.
L. McInnes, J. Healy, and S. Astels, “hdbscan: Hierarchical density based clustering,”
2017
Earlier work this paper cites.
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba, “Scene parsing through ade20k dataset,” in
2017
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár, “Panoptic segmentation,” in
2019
Earlier work this paper cites.
M. Grinvald, F. Furrer, T. Novkovic, J. J. Chung, C. Cadena, R. Siegwart, and J. Nieto, “Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery,”
2019
Earlier work this paper cites.
G. Narita, T. Seno, T. Ishikawa, and Y. Kaji, “Panopticfusion: Online volumetric semantic mapping at the level of stuff and things,” in
2019
Earlier work this paper cites.
X. Wang, S. Liu, X. Shen, C. Shen, and J. Jia, “Associatively segmenting instances and semantics in point clouds,” in
2019
Earlier work this paper cites.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” in
2020
Earlier work this paper cites.
B. Cheng, M. D. Collins, Y. Zhu, T. Liu, T. S. Huang, H. Adam, and L.-C. Chen, “Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation,” in
2020
Earlier work this paper cites.
S. Xie, J. Gu, D. Guo, C. R. Qi, L. Guibas, and O. Litany, “Pointcontrast: Unsupervised pre-training for 3d point cloud understanding,” in
2020
Earlier work this paper cites.
J. Lambert, Z. Liu, O. Sener, J. Hays, and V. Koltun, “MSeg: A composite dataset for multi-domain semantic segmentation,” in
2020
Earlier work this paper cites.
M. Roberts, J. Ramapuram, A. Ranjan, A. Kumar, M. A. Bautista, N. Paczan, R. Webb, and J. M. Susskind, “Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding,” in
2021
Earlier work this paper cites.
K. Sirohi, R. Mohan, D. Büscher, W. Burgard, and A. Valada, “Efficientlps: Efficient lidar panoptic segmentation,”
2021
Earlier work this paper cites.
M. Dahnert, J. Hou, M. Nießner, and A. Dai, “Panoptic 3d scene reconstruction from a single rgb image,”
2021
Cited alongside, same era.
J. T. Barron, B. Mildenhall, M. Tancik, P. Hedman, R. Martin-Brualla, and P. P. Srinivasan, “Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields,” in
2021
Cited alongside, same era.
S. Zhi, T. Laidlow, S. Leutenegger, and A. Davison, “In-place scene labelling and understanding with implicit scene representation,” in
2021
Cited alongside, same era.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark
2021
Cited alongside, same era.
Z. Liu, X. Qi, and C.-W. Fu, “3d-to-2d distillation for indoor scene parsing,” in
2021
Cited alongside, same era.
V. Tschernezki, I. Laina, D. Larlus, and A. Vedaldi, “Neural feature fusion fields: 3d distillation of self-supervised 2d image representations,” in
2022
Later among the works it cites.
G. Ghiasi, X. Gu, Y. Cui, and T.-Y. Lin, “Scaling open-vocabulary image segmentation with image-level labels,” in
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
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L. Jiang, S. Shi, Z. Tian, X. Lai, S. Liu, C.-W. Fu, and J. Jia, “Guided point contrastive learning for semi-supervised point cloud semantic segmentation,” in
2021
Cited alongside, same era.
B. Li, K. Q. Weinberger, S. Belongie, V. Koltun, and R. Ranftl, “Language-driven semantic segmentation,” in
2022
Cited alongside, same era.
B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar, “Masked-attention mask transformer for universal image segmentation,” in
2022
Cited alongside, same era.
W. K. Fong, R. Mohan, J. V. Hurtado, L. Zhou, H. Caesar, O. Beijbom, and A. Valada, “Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking,”
2022
Cited alongside, same era.
2022
Cited alongside, same era.
X. Fu, S. Zhang, T. Chen, Y. Lu, L. Zhu, X. Zhou, A. Geiger, and Y. Liao, “Panoptic nerf: 3d-to-2d label transfer for panoptic urban scene segmentation,” in
2022
Cited alongside, same era.
A. Kundu, K. Genova, X. Yin, A. Fathi, C. Pantofaru, L. Guibas, A. Tagliasacchi, F. Dellaert, and T. Funkhouser, “Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation,” in
2022
Cited alongside, same era.
2022
Later among the works it cites.
2023
Closest in time.
K. Blomqvist, F. Milano, J. J. Chung, L. Ott, and R. Siegwart, “Neural implicit vision-language feature fields,” in
2023
Closest in time.
2023
Closest in time.
Y. Siddiqui, L. Porzi, S. R. Bulò, N. Müller, M. Nießner, A. Dai, and P. Kontschieder, “Panoptic lifting for 3d scene understanding with neural fields,” in
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
M. Xu, Z. Zhang, F. Wei, H. Hu, and X. Bai, “Side adapter network for open-vocabulary semantic segmentation,” in
2023
Closest in time.
M. Yi, Q. Cui, H. Wu, C. Yang, O. Yoshie, and H. Lu, “A simple framework for text-supervised semantic segmentation,” in
2023
Closest in time.
F. Liang, B. Wu, X. Dai, K. Li, Y. Zhao, H. Zhang, P. Zhang, P. Vajda, and D. Marculescu, “Open-vocabulary semantic segmentation with mask-adapted clip,” in
2023
Closest in time.
S. Peng, K. Genova, C. Jiang, A. Tagliasacchi, M. Pollefeys, T. Funkhouser
2023
Closest in time.