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Latent Partition Implicit with Surface Codes for 3D Representation

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Deep implicit functions have shown remarkable shape modeling ability in various 3D computer vision tasks. One drawback is that it is hard for them to represent a 3D shape as multiple parts. Current solutions learn various primitives and blend the primitives directly in the spatial space, which still struggle to approximate the 3D shape accurately. To resolve this problem, we introduce a novel implicit representation to represent a single 3D shape as a set of parts in the latent space, towards both highly accurate and plausibly interpretable shape modeling. Our insight here is that both the part learning and the part blending can be conducted much easier in the latent space than in the spatial space. We name our method Latent Partition Implicit (LPI), because of its ability of casting the global shape modeling into multiple local part modeling, which partitions the global shape unity. LPI represents a shape as Signed Distance Functions (SDFs) using surface codes. Each surface code is a latent code representing a part whose center is on the surface, which enables us to flexibly employ intrinsic attributes of shapes or additional surface properties. Eventually, LPI can reconstruct both the shape and the parts on the shape, both of which are plausible meshes. LPI is a multi-level representation, which can partition a shape into different numbers of parts after training. LPI can be learned without ground truth signed distances, point normals or any supervision for part partition. LPI outperforms the latest methods under the widely used benchmarks in terms of reconstruction accuracy and modeling interpretability. Our code, data and models are available at https://github.com/chenchao15/LPI.

This work was supported by National Key R &D Program of China (2022YFC3800600, 2020YFF0304100), the National Natural Science Foundation of China (62272263, 62072268), and in part by Tsinghua-Kuaishou Institute of Future Media Data.

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References

  1. Atzmon, M., Lipman, Y.: Sal: sign agnostic learning of shapes from raw data. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  2. Atzmon, M., Lipman, Y.: SALD: sign agnostic learning with derivatives. In: International Conference on Learning Representations (2021)

    Google Scholar 

  3. Azinović, D., Martin-Brualla, R., Goldman, D.B., Nießner, M., Thies, J.: Neural rgb-d surface reconstruction. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6290–6301 (2022)

    Google Scholar 

  4. Ben-Shabat, Y., Koneputugodage, C.H., Gould, S.: Digs : divergence guided shape implicit neural representation for unoriented point clouds. CoRR abs/2106.10811 (2021)

    Google Scholar 

  5. Bernardini, F., Mittleman, J., Rushmeier, H., Silva, C., Taubin, G.: The ball-pivoting algorithm for surface reconstruction. IEEE Trans. Visual Comput. Graph. 5(4), 349–359 (1999)

    Article  Google Scholar 

  6. Bogo, F., Romero, J., Pons-Moll, G., Black, M.J.: Dynamic FAUST: registering human bodies in motion. In: IEEE Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  7. Boulch, A., Marlet, R.: Poco: Point convolution for surface reconstruction. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  8. Chabra, R., et al.: Deep local shapes: learning local sdf priors for detailed 3d reconstruction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 608–625. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58526-6_36

    Chapter  Google Scholar 

  9. Chang, A.X., et al.: ShapeNet: an Information-Rich 3D Model Repository. Technical reports. arXiv:1512.03012 [cs.GR], Stanford University – Princeton University – Toyota Technological Institute at Chicago (2015)

  10. Chen, C., Han, Z., Liu, Y.S., Zwicker, M.: Unsupervised learning of fine structure generation for 3D point clouds by 2D projection matching. In: IEEE International Conference on Computer Vision (2021)

    Google Scholar 

  11. Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  12. Chibane, J., Alldieck, T., Pons-Moll, G.: Implicit functions in feature space for 3d shape reconstruction and completion. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6968–6979 (2020)

    Google Scholar 

  13. Chibane, J., Mir, A., Pons-Moll, G.: Neural unsigned distance fields for implicit function learning. arXiv 2010.13938 (2020)

    Google Scholar 

  14. Crane, K., Weischedel, C., Wardetzky, M.: The heat method for distance computation. Commun. ACM 60(11), 90–99 (2017)

    Article  Google Scholar 

  15. Darmon, F., Bascle, B., Devaux, J.C., Monasse, P., Aubry, M.: Improving neural implicit surfaces geometry with patch warping. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6260–6269 (2022)

    Google Scholar 

  16. Deng, B., Genova, K., Yazdani, S., Bouaziz, S., Hinton, G.E., Tagliasacchi, A.: Cvxnet: learnable convex decomposition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 31–41 (2020)

    Google Scholar 

  17. Erler, P., Guerrero, P., Ohrhallinger, S., Mitra, N.J., Wimmer, M.: Points2Surf learning implicit surfaces from point clouds. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 108–124. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58558-7_7

    Chapter  Google Scholar 

  18. Feng, W., Li, J., Cai, H., Luo, X., Zhang, J.: Neural points: point cloud representation with neural fields for arbitrary upsampling. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  19. Filoscia, I., Alderighi, T., Giorgi, D., Malomo, L., Callieri, M., Cignoni, P.: Optimizing object decomposition to reduce visual artifacts in 3d printing. Comput. Graph. Forum 39(2), 423–434 (2020)

    Article  Google Scholar 

  20. Gal, R., Bermano, A., Zhang, H., Cohen-Or, D.: MRGAN: multi-rooted 3d shape generation with unsupervised part disentanglement. CoRR abs/2007.12944 (2020)

    Google Scholar 

  21. Genova, K., Cole, F., Sud, A., Sarna, A., Funkhouser, T.: Local deep implicit functions for 3d shape. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  22. Genova, K., Cole, F., Vlasic, D., Sarna, A., Freeman, W.T., Funkhouser, T.: Learning shape templates with structured implicit functions. In: International Conference on Computer Vision (2019)

    Google Scholar 

  23. Giebenhain, S., Goldluecke, B.: Air-nets: an attention-based framework for locally conditioned implicit representations. In: 2021 International Conference on 3D Vision. IEEE (2021)

    Google Scholar 

  24. Gropp, A., Yariv, L., Haim, N., Atzmon, M., Lipman, Y.: Implicit geometric regularization for learning shapes. arXiv 2002.10099 (2020)

    Google Scholar 

  25. Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: A papier-mâché approach to learning 3D surface generation. In: IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  26. Han, Z., Chen, C., Liu, Y.S., Zwicker, M.: Drwr: a differentiable renderer without rendering for unsupervised 3D structure learning from silhouette images. In: International Conference on Machine Learning (2020)

    Google Scholar 

  27. Han, Z., Chen, C., Liu, Y.S., Zwicker, M.: ShapeCaptioner: generative caption network for 3D shapes by learning a mapping from parts detected in multiple views to sentences. In: ACM International Conference on Multimedia (2020)

    Google Scholar 

  28. Han, Z., et al.: 3D2SeqViews: aggregating sequential views for 3D global feature learning by cnn with hierarchical attention aggregation. IEEE Trans. Image Process. 28(8), 3986–3999 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  29. Han, Z., Qiao, G., Liu, Y.-S., Zwicker, M.: SeqXY2SeqZ: structure learning for 3d shapes by sequentially predicting 1d occupancy segments from 2d coordinates. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12369, pp. 607–625. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58586-0_36

    Chapter  Google Scholar 

  30. Han, Z., Shang, M., Liu, Y.S., Zwicker, M.: View inter-prediction GAN: unsupervised representation learning for 3D shapes by learning global shape memories to support local view predictions. In: AAAI, pp. 8376–8384 (2019)

    Google Scholar 

  31. Han, Z., et al.: SeqViews2SeqLabels: learning 3D global features via aggregating sequential views by rnn with attention. IEEE Trans. Image Process. 28(2), 672–685 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  32. Han, Z., Shang, M., Wang, X., Liu, Y.S., Zwicker, M.: Y2Seq2Seq: cross-modal representation learning for 3D shape and text by joint reconstruction and prediction of view and word sequences. In: AAAI, pp. 126–133 (2019)

    Google Scholar 

  33. Han, Z., Wang, X., Liu, Y.S., Zwicker, M.: Multi-angle point cloud-vae:unsupervised feature learning for 3D point clouds from multiple angles by joint self-reconstruction and half-to-half prediction. In: IEEE International Conference on Computer Vision (2019)

    Google Scholar 

  34. Hasselgren, J., Hofmann, N., Munkberg, J.: Shape, light and material decomposition from images using monte carlo rendering and denoising. arXiv abs/2206.03380 (2022)

    Google Scholar 

  35. Hu, T., Han, Z., Zwicker, M.: 3D shape completion with multi-view consistent inference. In: AAAI (2020)

    Google Scholar 

  36. Jain, A., Mildenhall, B., Barron, J.T., Abbeel, P., Poole, B.: Zero-shot text-guided object generation with dream fields (2022)

    Google Scholar 

  37. Jiang, C., Sud, A., Makadia, A., Huang, J., Nießner, M., Funkhouser, T.: Local implicit grid representations for 3D scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  38. Jiang, Y., Ji, D., Han, Z., Zwicker, M.: SDFDiff: differentiable rendering of signed distance fields for 3D shape optimization. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  39. Kazhdan, M.M., Hoppe, H.: Screened poisson surface reconstruction. ACM Trans. Graph. 32(3), 29:1-29:13 (2013)

    Article  MATH  Google Scholar 

  40. Li, T., Wen, X., Liu, Y.S., Su, H., Han, Z.: Learning deep implicit functions for 3D shapes with dynamic code clouds. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  41. Liao, Y., Donné, S., Geiger, A.: Deep marching cubes: learning explicit surface representations. In: Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  42. Lin, C.H., Wang, C., Lucey, S.: Sdf-srn: learning signed distance 3d object reconstruction from static images. In: Advances in Neural Information Processing Systems (2020)

    Google Scholar 

  43. Littwin, G., Wolf, L.: Deep meta functionals for shape representation. In: IEEE International Conference on Computer Vision (2019)

    Google Scholar 

  44. Liu, M., Zhang, X., Su, H.: Meshing point clouds with predicted intrinsic-extrinsic ratio guidance. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 68–84. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58598-3_5

    Chapter  Google Scholar 

  45. Liu, S., Zhang, Y., Peng, S., Shi, B., Pollefeys, M., Cui, Z.: DIST: rendering deep implicit signed distance function with differentiable sphere tracing. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  46. Liu, S.L., Guo, H.X., Pan, H., Wang, P., Tong, X., Liu, Y.: Deep implicit moving least-squares functions for 3D reconstruction. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

  47. Liu, S., Saito, S., Chen, W., Li, H.: Learning to infer implicit surfaces without 3D supervision. In: Advances in Neural Information Processing Systems (2019)

    Google Scholar 

  48. Liu, X., Han, Z., Liu, Y.S., Zwicker, M.: Point2Sequence: learning the shape representation of 3D point clouds with an attention-based sequence to sequence network. In: AAAI, pp. 8778–8785 (2019)

    Google Scholar 

  49. Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. Comput. Graph. 21(4), 163–169 (1987)

    Article  Google Scholar 

  50. Ma, B., Han, Z., Liu, Y.S., Zwicker, M.: Neural-pull: learning signed distance functions from point clouds by learning to pull space onto surfaces. In: International Conference on Machine Learning (2021)

    Google Scholar 

  51. Ma, B., Liu, Y.S., Zwicker, M., Han, Z.: Reconstructing surfaces for sparse point clouds with on-surface priors. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  52. Ma, B., Liu, Y.S., Zwicker, M., Han, Z.: Surface reconstruction from point clouds by learning predictive context priors. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  53. Martel, J.N.P., Lindell, D.B., Lin, C.Z., Chan, E.R., Monteiro, M., Wetzstein, G.: ACORN: adaptive coordinate networks for neural scene representation. CoRR abs/2105.02788 (2021)

    Google Scholar 

  54. Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: Learning 3D reconstruction in function space. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  55. Mi, Z., Luo, Y., Tao, W.: Ssrnet: scalable 3D surface reconstruction network. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  56. Michalkiewicz, M., Pontes, J.K., Jack, D., Baktashmotlagh, M., Eriksson, A.P.: Deep level sets: implicit surface representations for 3D shape inference. CoRR abs/1901.06802 (2019)

    Google Scholar 

  57. Michel, O., Bar-On, R., Liu, R., Benaim, S., Hanocka, R.: Text2mesh: text-driven neural stylization for meshes. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  58. 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 

  59. Yavartanoo, M., Chung, J., Neshatavar, R., Lee, K.M.: 3dias: 3d shape reconstruction with implicit algebraic surfaces. In: International Conference on Computer Vision (2021)

    Google Scholar 

  60. Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. arXiv:2201.05989 (2022)

  61. Muntoni, A., Livesu, M., Scateni, R., Sheffer, A., Panozzo, D.: Axis-aligned height-field block decomposition of 3d shapes. ACM Trans. Graph. 37(5), 169:1-169:15 (2018)

    Article  Google Scholar 

  62. Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: Learning implicit 3D representations without 3D supervision. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  63. Novotny, D., et al.: Keytr: keypoint transporter for 3d reconstruction of deformable objects in videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5595–5604 (2022)

    Google Scholar 

  64. Oechsle, M., Peng, S., Geiger, A.: UNISURF: unifying neural implicit surfaces and radiance fields for multi-view reconstruction. CoRR abs/2104.10078 (2021)

    Google Scholar 

  65. Ohtake, Y., Belyaev, A.G., Alexa, M., Turk, G., Seidel, H.: Multi-level partition of unity implicits. ACM Trans. Graph. 22(3), 463–470 (2003)

    Article  Google Scholar 

  66. Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  67. Paschalidou, D., van Gool, L., Geiger, A.: Learning unsupervised hierarchical part decomposition of 3d objects from a single rgb image. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  68. Paschalidou, D., Gool, L.V., Geiger, A.: Learning unsupervised hierarchical part decomposition of 3d objects from a single RGB image. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1057–1067 (2020)

    Google Scholar 

  69. Paschalidou, D., Katharopoulos, A., Geiger, A., Fidler, S.: Neural parts: learning expressive 3d shape abstractions with invertible neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3204–3215 (2021)

    Google Scholar 

  70. Peng, S., Jiang, C.M., Liao, Y., Niemeyer, M., Pollefeys, M., Geiger, A.: Shape as points: a differentiable poisson solver. In: Advances in Neural Information Processing Systems (2021)

    Google Scholar 

  71. Rebain, D., Li, K., Sitzmann, V., Yazdani, S., Yi, K.M., Tagliasacchi, A.: Deep medial fields. CoRR abs/2106.03804 (2021)

    Google Scholar 

  72. Rückert, D., Franke, L., Stamminger, M.: Adop: approximate differentiable one-pixel point rendering. arXiv:2110.06635 (2021)

  73. Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., Li, H.: PIFu: pixel-aligned implicit function for high-resolution clothed human digitization (2019)

    Google Scholar 

  74. Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: radiance fields without neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  75. Sharma, G., et al.: Surfit: learning to fit surfaces improves few shot learning on point clouds. CoRR abs/2112.13942 (2021)

    Google Scholar 

  76. Sitzmann, V., Martel, J.N., Bergman, A.W., Lindell, D.B., Wetzstein, G.: Implicit neural representations with periodic activation functions. In: Advances in Neural Information Processing Systems (2020)

    Google Scholar 

  77. Sitzmann, V., Zollhöfer, M., Wetzstein, G.: Scene representation networks: continuous 3D-structure-aware neural scene representations. In: Advances in Neural Information Processing Systems (2019)

    Google Scholar 

  78. Peng, S., Niemeyer, M., Mescheder, L., Pollefeys, M., Geiger, A.: Convolutional occupancy networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 523–540. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58580-8_31

    Chapter  Google Scholar 

  79. Takikawa, T., et al.: Neural geometric level of detail: Real-time rendering with implicit 3D shapes. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

  80. Tang, J., Lei, J., Xu, D., Ma, F., Jia, K., Zhang, L.: Sa-convonet: sign-agnostic optimization of convolutional occupancy networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2021)

    Google Scholar 

  81. Tatarchenko, M., Richter, S.R., Ranftl, R., Li, Z., Koltun, V., Brox, T.: What do single-view 3D reconstruction networks learn? In: The IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  82. Tretschk, E., Tewari, A., Golyanik, V., Zollhöfer, M., Stoll, C., Theobalt, C.: PatchNets: patch-based generalizable deep implicit 3d shape representations. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 293–309. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58517-4_18

    Chapter  Google Scholar 

  83. Wang, W., Xu, Q., Ceylan, D., Mech, R., Neumann, U.: DISN: deep implicit surface network for high-quality single-view 3D reconstruction. In: Advances In Neural Information Processing Systems (2019)

    Google Scholar 

  84. Wen, X., Han, Z., Cao, Y.P., Wan, P., Zheng, W., Liu, Y.S.: Cycle4completion: unpaired point cloud completion using cycle transformation with missing region coding. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

  85. Wen, X., Li, T., Han, Z., Liu, Y.S.: Point cloud completion by skip-attention network with hierarchical folding. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  86. Wen, X., et al.: Pmp-net: point cloud completion by learning multi-step point moving paths. In: IEEE Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

  87. Wen, X., Zhou, J., Liu, Y.S., Su, H., Dong, Z., Han, Z.: 3D shape reconstruction from 2D images with disentangled attribute flow. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  88. Williams, F., Parent-Lévesque, J., Nowrouzezahrai, D., Panozzo, D., Yi, K.M., Tagliasacchi, A.: Voronoinet : general functional approximators with local support. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1069–1073 (2020)

    Google Scholar 

  89. Williams, F., Schneider, T., Silva, C., Zorin, D., Bruna, J., Panozzo, D.: Deep geometric prior for surface reconstruction. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  90. Wu, Y., Sun, Z.: DFR: differentiable function rendering for learning 3D generation from images. Comput. Graph. Forum 39(5), 241–252 (2020)

    Article  MathSciNet  Google Scholar 

  91. Xiang, P., et al.: Snowflakenet: point cloud completion by snowflake point deconvolution with skip-transformer. In: IEEE International Conference on Computer Vision (2021)

    Google Scholar 

  92. Yao, C.H., Hung, W.C., Jampani, V., Yang, M.H.: Discovering 3d parts from image collections. In: IEEE International Conference on Computer Vision (2021)

    Google Scholar 

  93. Yao, S., Yang, F., Cheng, Y., Mozerov, M.G.: 3d shapes local geometry codes learning with sdf. In: IEEE International Conference on Computer Vision Workshops, pp. 2110–2117 (2021)

    Google Scholar 

  94. Yavartanoo, M., Chung, J., Neshatavar, R., Lee, K.M.: 3dias: 3d shape reconstruction with implicit algebraic surfaces. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 12446–12455 (2021)

    Google Scholar 

  95. Yifan, W., Wu, S., Oztireli, C., Sorkine-Hornung, O.: Iso-points: optimizing neural implicit surfaces with hybrid representations. CoRR abs/2012.06434 (2020)

    Google Scholar 

  96. Yu, Z., Peng, S., Niemeyer, M., Sattler, T., Geiger, A.: Monosdf: exploring monocular geometric cues for neural implicit surface reconstruction. arXiv abs/2022.00665 (2022)

    Google Scholar 

  97. Zakharov, S., Kehl, W., Bhargava, A., Gaidon, A.: Autolabeling 3D objects with differentiable rendering of sdf shape priors. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  98. Zhao, W., Lei, J., Wen, Y., Zhang, J., Jia, K.: Sign-agnostic implicit learning of surface self-similarities for shape modeling and reconstruction from raw point clouds. CoRR abs/2012.07498 (2020)

    Google Scholar 

  99. Zhu, Z., Peng, S., et al.: Nice-slam: neural implicit scalable encoding for slam. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

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Chen, C., Liu, YS., Han, Z. (2022). Latent Partition Implicit with Surface Codes for 3D Representation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13663. Springer, Cham. https://doi.org/10.1007/978-3-031-20062-5_19

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