[1] A. Bansal, K. Sikka, G. Sharma, R. Chellappa, and A. Divakaran. Zero-shot object detection. In Proceedings of the European Conference on Computer Vision (ECCV), pages 384-400, 2018. 7
[2] A. Bendale and T. Boult. Towards open world recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1893-1902, 2015. 3
[3] J. Cen, P. Yun, S. Zhang, J. Cai, D. Luan, M. Tang, M. Liu, and M. Yu Wang. Open-world semantic segmentation for lidar point clouds. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXVIII, pages 318-334. Springer, 2022. 3
[4] S. Chen, J. Fang, Q. Zhang, W. Liu, and X. Wang. Hierarchical aggregation for 3d instance segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15467-15476, 2021. 2
[5] B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar. Masked-attention mask transformer for universal image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1290–1299, 2022. 2
[6] B. Cheng, A. Schwing, and A. Kirillov. Per-pixel classification is not all you need for semantic segmentation. Advances in Neural Information Processing Systems, 34:17864-17875, 2021. 2
[7] J. Chibane, F. Engelmann, T. Anh Tran, and G. Pons-Moll. Box2mask: Weakly supervised 3d semantic instance segmentation using bounding boxes. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXI, pages 681-699. Springer, 2022. 2
[8] A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5828-5839, 2017. 5
[9] A. Dhamija, M. Gunther, J. Ventura, and T. Boult. The overlooked elephant of object detection: Open set. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1021-1030, 2020. 7
[10] F. Engelmann, M. Bokeloh, A. Fathi, B. Leibe, and M. Nießner. 3d-mpa: Multi-proposal aggregation for 3d semantic instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9031–9040, 2020. 2
[11] A. Gupta, S. Narayan, K. Joseph, S. Khan, F. S. Khan, and M. Shah. Ow-detr: Open-world detection transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9235–9244, 2022. 1, 3, 7, 8
[12] H. Ha and S. Song. Semantic abstraction: Open-world 3d scene understanding from 2d vision-language models. In 6th Annual Conference on Robot Learning, 2022. 3
[13] L. Han, T. Zheng, L. Xu, and L. Fang. Occuseg: Occupancy-aware 3d instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2940-2949, 2020. 2
[14] T. He, C. Shen, and A. Van Den Hengel. Dyco3d: Robust instance segmentation of 3d point clouds through dynamic convolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 354-363, 2021. 2
[15] J. Hou, A. Dai, and M. Nießner. 3d-sis: 3d semantic instance segmentation of rgb-d scans. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4421-4430, 2019. 2