Abstract
Cross-modal retrieval, as an important emerging foundational information retrieval task, benefits from recent advances in multimodal technologies. However, current cross-modal retrieval methods mainly focus on the interaction between textual information and 2D images, lacking research on 3D data, especially point clouds at scene level, despite the increasing role point clouds play in daily life. Therefore, in this paper, we proposed a cross-modal point cloud retrieval benchmark that focuses on using text or images to retrieve point clouds of indoor scenes. Given the high cost of obtaining point cloud compared to text and images, we first designed a pipeline to automatically generate a large number of indoor scenes and their corresponding scene graphs. Based on this pipeline, we collected a balanced dataset called CRISP, which contains 10K point cloud scenes along with their corresponding scene images and descriptions. We then used state-of-the-art models to design baseline methods on CRISP. Our experiments demonstrated that point cloud retrieval accuracy is much lower than cross-modal retrieval of 2D images, especially for textual queries. Furthermore, we proposed ModalBlender, a tri-modal framework which can greatly improve the Text-PointCloud retrieval performance. Through extensive experiments, CRISP proved to be a valuable dataset and worth researching. (Dataset can be downloaded at https://github.com/CRISPdataset/CRISP.)
F. Yu and Z. Wang—Equal contribution.
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References
Chang, A., et al.: Matterport3D: learning from RGB-D data in indoor environments. arXiv preprint arXiv:1709.06158 (2017)
Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)
Contributors, S.: SpConv: spatially sparse convolution library. https://github.com/traveller59/spconv (2022)
Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: ScanNet: richly-annotated 3D reconstructions of indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5828–5839 (2017)
Greff, K., et al.: Kubric: a scalable dataset generator. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3749–3761 (2022)
Handa, A., Pătrăucean, V., Stent, S., Cipolla, R.: SceneNet: an annotated model generator for indoor scene understanding. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 5737–5743. IEEE (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Johnson, J., Hariharan, B., Van Der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., Girshick, R.: CLEVR: a diagnostic dataset for compositional language and elementary visual reasoning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2901–2910 (2017)
Li, J., Li, D., Savarese, S., Hoi, S.: BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597 (2023)
Li, J., Li, D., Xiong, C., Hoi, S.: BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International Conference on Machine Learning, pp. 12888–12900. PMLR (2022)
Li, W., et al.: InteriorNet: mega-scale multi-sensor photo-realistic indoor scenes dataset. arXiv preprint arXiv:1809.00716 (2018)
Liggett, R.S.: Automated facilities layout: past, present and future. Autom. Constr. 9(2), 197–215 (2000)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
Plummer, B.A., Wang, L., Cervantes, C.M., Caicedo, J.C., Hockenmaier, J., Lazebnik, S.: Flickr30k entities: collecting region-to-phrase correspondences for richer image-to-sentence models. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2641–2649 (2015)
Qi, C.R., Litany, O., He, K., Guibas, L.J.: Deep hough voting for 3D object detection in point clouds. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9277–9286 (2019)
Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR (2021)
Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The Princeton shape benchmark. In: Proceedings Shape Modeling Applications, 2004, pp. 167–178. IEEE (2004)
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. ECCV 5(7576), 746–760 (2012). https://doi.org/10.1007/978-3-642-33715-4_54
Song, D., Nie, W.Z., Li, W.H., Kankanhalli, M., Liu, A.A.: Monocular image-based 3-D model retrieval: a benchmark. IEEE Trans. Cybern. 52(8), 8114–8127 (2021)
Song, S., Lichtenberg, S.P., Xiao, J.: Sun RGB-D: a RGB-D scene understanding benchmark suite. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 567–576 (2015)
Song, S., Yu, F., Zeng, A., Chang, A.X., Savva, M., Funkhouser, T.: Semantic scene completion from a single depth image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1746–1754 (2017)
Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 945–953 (2015)
Wald, J., Dhamo, H., Navab, N., Tombari, F.: Learning 3D semantic scene graphs from 3D indoor reconstructions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3961–3970 (2020)
Wang, K., Yin, Q., Wang, W., Wu, S., Wang, L.: A comprehensive survey on cross-modal retrieval. arXiv preprint arXiv:1607.06215 (2016)
Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912–1920 (2015)
Xu, Y., Tong, X., Stilla, U.: Voxel-based representation of 3D point clouds: methods, applications, and its potential use in the construction industry. Autom. Constr. 126, 103675 (2021)
Yi, K., et al.: CLEVRER: collision events for video representation and reasoning. arXiv preprint arXiv:1910.01442 (2019)
Yuan, J., et al.: SHREC’19 Track: extended 2D scene sketch-based 3D scene retrieval. Training 18, 70 (2019)
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Yu, F. et al. (2024). Towards Cross-Modal Point Cloud Retrieval for Indoor Scenes. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14557. Springer, Cham. https://doi.org/10.1007/978-3-031-53302-0_7
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