[35] Justin Kerr, Chung Min Kim, Ken Goldberg, Angjoo Kanazawa, and Matthew Tancik. LERF: Language Embedded Radiance Fields. In ICCV, 2023.
[36] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick. Segment anything. In ICCV, 2023.
[37] Sosuke Kobayashi, Eiichi Matsumoto, and Vincent Sitzmann. Decomposing NeRF for Editing via Feature Field Distillation. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[38] Hema Koppula, Abhishek Anand, Thorsten Joachims, and Ashutosh Saxena. Semantic Labeling of 3D Point Clouds for Indoor Scenes. In Neural Information Processing Systems (NeurIPS), 2011.
[39] Lars Kreuzberg, Idil Esen Zulfikar, Sabarinath Mahadevan, Francis Engelmann, and Bastian Leibe. 4D-STOP: Panoptic Segmentation of 4D Lidar using Spatio-Temporal Object Proposal Generation and Aggregation. In European Conference on Computer Vision (ECCV) Workshops, 2022.
[40] Weicheng Kuo, Yin Cui, Xiuye Gu, AJ Piergiovanni, and Anelia Angelova. Open-Vocabulary Object Detection upon Frozen Vision and Language Models. In International Conference on Learning Representations (ICLR), 2023.
[41] Jean Lahoud, Bernard Ghanem, Marc Pollefeys, and Martin R. Oswald. 3D Instance Segmentation via Multi-task Metric Learning. In Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[42] Loic Landrieu and Martin Simonovsky. Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs. In Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[43] Boyi Li, Kilian Q. Weinberger, Serge Belongie, Vladlen Koltun, and Rene Ranftl. Language-driven Semantic Segmentation. In International Conference on Learning Representations (ICLR), 2022.
[44] Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. PointCNN: Convolution on X-transformed Points. In Neural Information Processing Systems (NeurIPS), 2018.
[45] Feng Liang, Bichen Wu, Xiaoliang Dai, Kunpeng Li, Yinan Zhao, Hang Zhang, Peizhao Zhang, Peter Vajda, and Diana Marculescu. Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP. In Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[46] Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully Convolutional Networks for Semantic Segmentation. In Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
[47] Yan Lu and Christopher Rasmussen. Simplified Markov Random Fields for Efficient Semantic Labeling of 3D Point Clouds. In International Conference on Intelligent Robots and Systems (ICIRS), 2012.
[48] Chao Ma, Yu-Hao Yang, Yanfeng Wang, Ya Zhang, and Weidi Xie. Open-vocabulary Semantic Segmentation with Frozen Vision-Language Models. In *British Machine Vision Conference (BMVC)*, 2022.
[49] Kirill Mazur, Edgar Sucar, and Andrew Davison. Feature-Realistic Neural Fusion for Real-Time, Open Set Scene Understanding. In International Conference on Robotics and Automation (ICRA), 2023.
[50] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. In Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[51] Maxime Oquab, Timothée Darcet, Theo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Russell Howes, Po-Yao Huang, Hu Xu, Vasu Sharma, Shang-Wen Li, Wojciech Galuba, Mike Rabbat, Mido Assran, Nicolas Ballas, Gabriel Synnaeve, Ishan Misra, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. DINOV2: Learning Robust Visual Features without Supervision. arXiv preprint arXiv:2304.07193, 2023.
[52] Songyou Peng, Kyle Genova, Chiyu "Max" Jiang, Andrea Tagliasacchi, Marc Pollefeys, and Thomas Funkhouser. OpenScene: 3D Scene Understanding with Open Vocabularies. In Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[53] Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. In Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[54] Charles R. Qi, Li Yi, Hao Su, and Leonidas J. Guibas. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. In Neural Information Processing Systems (NeurIPS), 2017.