Acknowledgement
본 연구는 2024년 경기대학교 대학원 연구원장학생 장학금 지원에 의하여 수행되었음.
References
- B. Yang, J. Wang, R. Clark, Q. Hu, S. Wang, A. Markham, and N. Trigoni, "Learning object bounding boxes for 3D instance segmentation on point clouds," In Proceedings of the Nueral Information Processing Systems (NeurlPS), 2019.
- S. Liu, S. Yu, S. Wu, H. Chen, and T. Liu, "Learning gaussian instance segmentation in point clouds," arXiv preprint arXiv:2007.09860, 2020.
- T. Vu, K. Kim, T. Luu, T. Nguyen, and C. D. Yoo, "SoftGroup for 3D instance segmentation on point clouds," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
- Z. Liang, Z. Li, S. Xu, M. Tan, and K. Jia, "Instance segmentation in 3D Scenes using semantic superpoint tree networks," In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021.
- J. Schult, F. Engelmann, A. Hermans, O. Litany, S. Tang, and B. Leibe, "Mask3D: Mask transformer for 3D semantic instance segmentation," In Proceedings of the International Conference on Robotics and Automation (ICRA), 2023.
- S. Song and I. Kim, "T3DIS: Transformer-based 3D instance segmentation with auxiliary denoising learning," In Proceedings of the Journal of Institue of Control, Robotics and Systems(J Inst Contr Robot Syst), Vol.29, No.12, pp, 954-965, 2023.
- A. Takmaz, E. Fedele, R. Sumner, M. Pollefeys, F. Tombari, and F. Engelmann, "OpenMask3D: Open-vocabulary 3D instance segmentation," In Proceedings of the Nueral Information Processing Systems (NeurlPS), 2023.
- Z. Huang, X. Wu, X. Chen, H. Zhao, L. Zhu, and J. Lasenby, "OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation," preprint arXiv:2309.00616, 2023.
- S. Lu, H. Chang, E. Jing, A. Boularias, and K. Bekris, "OVIR-3D: Open-vocabulary 3D instance retrieval without training on 3D data," In Proceedings of the Conference on Robot Learning(CoRL), 2023.
- R. Ding, J. Yang,, C. Xue, W. Zhang, S. Bai and X. Qi, "Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding," preprint arXiv:2308.00353, 2023.
- A. Radford et al., "Learning transferable visual models from natural language supervision," preprint arXiv:2103.00020, 2021.
- C. Jia et al., "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision," In Proceedings of the International Conference on Machine Learning(ICML), 2021.
- G. Ghiasi, X. Gu, Y. Cui, and T. Lin, "Scaling Open-vocabulary image segmentation with image-level labels," In Proceedings of the roceedings of the European Conference on Computer Vision (ECCV), 2022.
- J. Ding, N. Xue, G. Xia, and D. Dai, "Decoupling zero-shot semantic segmentation," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
- 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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
- J. Qin et al., "FreeSeg: Unified, universal and open-vocabulary image segmentation," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
- Y. Yang, X. Wu, T. He, H. Zhao, and X. Liu, "SAM3D: Segment anything in 3D scenes," arXiv preprint arXiv:2306.03908.
- A. Kirillov et al., "Segment Anything," In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023.
- R. Chen et al., "CLIP2Scene: Towards label-efficient 3D scene understanding by CLIP," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
- S. Peng, K. Genova, C. Jiang, A. Tagliasacchi, M. Pollefeys, and T. Funkhouser, "OpenScene: 3D Scene understanding with open vocabularies," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
- X. Zhou, R. Girdhar, A. Joulin, P. Krahenbuhl, and I. Misra, "Detecting twenty-thousand classes using image-level supervision," In Proceedings of the roceedings of the European Conference on Computer Vision (ECCV), 2022.
- C. Choy, J. Gwak, and S. Savarese "4D Spatio-Temporal ConvNets: Minkowski convolutional neural networks," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
- X. Wu, Y. Lao, L. Jiang, X. Liu, and H. Zhao, "Point Transformer V2: Grouped vector attention and partition-based pooling," In Proceedings of the Nueral Information Processing Systems (NeurlPS), 2022.