3D-Producer: A Hybrid and User-Friendly 3D Reconstruction System

1. Allène, C., Pons, J., Keriven, R.: Seamless image-based texture atlases using multi-band blending. In: ICPR, pp. 1–4. IEEE Computer Society (2008)

   Google Scholar 

2. Baumberg, A.: Blending images for texturing 3d models. In: Rosin, P.L., Marshall, A.D. (eds.) BMVC, pp. 1–10 (2002)

   Google Scholar 

3. Cernea, D.: OpenMVS: Multi-view stereo reconstruction library (2020). http://cdcseacave.github.io/openMVS

4. Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3d object detection network for autonomous driving. In: CVPR, pp. 1907–1915 (2017)

   Google Scholar 

5. Contributors, P.: Paddleseg, end-to-end image segmentation kit based on paddlepaddle. http://github.com/PaddlePaddle/PaddleSeg (2019)

6. Deng, J., Pan, Y., Yao, T., Zhou, W., Li, H., Mei, T.: Single shot video object detector. IEEE Trans. Multimedia 23, 846–858 (2020)

   Article  Google Scholar 

7. Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F.J., Marín-Jiménez, M.J.: Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognit. 47(6), 2280–2292 (2014)

   Google Scholar 

8. Gkioxari, G., Johnson, J., Malik, J.: Mesh R-CNN. In: ICCV. IEEE (2019)

   Google Scholar 

9. Griwodz, C., Gasparini, S., Calvet, L., Gurdjos, P., Castan, F., Maujean, B., Lillo, G.D., Lanthony, Y.: Alicevision Meshroom: an open-source 3D reconstruction pipeline. In: Proceedings of the 12th ACM Multimedia Systems Conference - MMSys 2021. ACM Press (2021)

   Google Scholar 

10. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

    Google Scholar 

11. Li, Y., Yao, T., Pan, Y., Mei, T.: Contextual transformer networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2022)

    Google Scholar 

12. Liu, Y., Chu, L., Chen, G., Wu, Z., Chen, Z., Lai, B., Hao, Y.: Paddleseg: a high-efficient development toolkit for image segmentation (2021)

    Google Scholar 

13. Lombardi, S., Simon, T., Saragih, J.M., Schwartz, G., Lehrmann, A.M., Sheikh, Y.: Neural volumes: learning dynamic renderable volumes from images. ACM Trans. Graph. 38(4), 65:1–65:14 (2019)

    Google Scholar 

14. Long, F., Qiu, Z., Pan, Y., Yao, T., Luo, J., Mei, T.: Stand-alone inter-frame attention in video models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3192–3201 (2022)

    Google Scholar 

15. Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3d surface construction algorithm. In: Stone, M.C. (ed.) SIGGRAPH. ACM (1987)

    Google Scholar 

16. Mescheder, L.M., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3d reconstruction in function space. In: CVPR, pp. 4460–4470 (2019)

    Google Scholar 

17. Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405–421. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_24

    Chapter  Google Scholar 

18. Pan, Y., Chen, Y., Bao, Q., Zhang, N., Yao, T., Liu, J., Mei, T.: Smart director: an event-driven directing system for live broadcasting. TOMM 17(4), 1–18 (2021)

    Article  Google Scholar 

19. Pan, Y., Li, Y., Yao, T., Mei, T., Li, H., Rui, Y.: Learning deep intrinsic video representation by exploring temporal coherence and graph structure. In: IJCAI, pp. 3832–3838 (2016)

    Google Scholar 

20. Pan, Y., Yao, T., Li, Y., Mei, T.: X-linear attention networks for image captioning. In: CVPR (2020)

    Google Scholar 

21. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: CVPR, pp. 652–660 (2017)

    Google Scholar 

22. Rematas, K., Liu, A., Srinivasan, P.P., Barron, J.T., Tagliasacchi, A., Funkhouser, T.A., Ferrari, V.: Urban radiance fields. CoRR abs/2111.14643 (2021)

    Google Scholar 

23. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2016)

    Article  Google Scholar 

24. Schönberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: CVPR (2016)

    Google Scholar 

25. Schönberger, J.L., Zheng, E., Pollefeys, M., Frahm, J.M.: Pixelwise view selection for unstructured multi-view stereo. In: ECCV (2016)

    Google Scholar 

26. Sitzmann, V., Thies, J., Heide, F., Nießner, M., Wetzstein, G., Zollhöfer, M.: Deepvoxels: Learning persistent 3d feature embeddings. In: CVPR, pp. 2437–2446 (2019)

    Google Scholar 

27. Wang, P., Liu, L., Liu, Y., Theobalt, C., Komura, T., Wang, W.: Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In: NeurIPS, pp. 27171–27183 (2021)

    Google Scholar 

28. Yao, T., Li, Y., Pan, Y., Wang, Y., Zhang, X.P., Mei, T.: Dual vision transformer. arXiv preprint arXiv:2207.04976 (2022)

29. Yao, T., Pan, Y., Li, Y., Ngo, C.W., Mei, T.: Wave-vit: unifying wavelet and transformers for visual representation learning. In: Proceedings of the European Conference on Computer Vision (ECCV) (2022)

    Google Scholar 

30. Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)

    Article  Google Scholar 

Download references