Abstract
Learning efficient representations of local features is a key challenge in feature volume-based 3D neural mapping, especially in large-scale environments. In this paper, we introduce Decomposition-based Neural Mapping (DNMap), a storage-efficient large-scale 3D mapping method that employs a discrete representation based on a decomposition strategy. This decomposition strategy aims to efficiently capture repetitive and representative patterns of shapes by decomposing each discrete embedding into component vectors that are shared across the embedding space. Our DNMap optimizes a set of component vectors, rather than entire discrete embeddings, and learns composition rather than indexing the discrete embeddings. Furthermore, to complement the mapping quality, we additionally learn low-resolution continuous embeddings that require tiny storage space. By combining these representations with a shallow neural network and an efficient octree-based feature volume, our DNMap successfully approximates signed distance functions and compresses the feature volume while preserving mapping quality. Our source code is available at https://github.com/minseong-p/dnmap.
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References
Azinović, D., Martin-Brualla, R., Goldman, D.B., Nießner, M., Thies, J.: Neural RGB-D surface reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6290–6301 (2022)
Bengio, Y., Léonard, N., Courville, A.: Estimating or propagating gradients through stochastic neurons for conditional computation (2013)
Deng, J., et al.: NeRF-LOAM: neural implicit representation for large-scale incremental lidar odometry and mapping. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 8218–8227 (2023)
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361 (2012). https://doi.org/10.1109/CVPR.2012.6248074
Gropp, A., Yariv, L., Haim, N., Atzmon, M., Lipman, Y.: Implicit geometric regularization for learning shapes. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, pp. 3789–3799. PMLR (2020). https://proceedings.mlr.press/v119/gropp20a.html
Isaacson, S., Kung, P.C., Ramanagopal, M., Vasudevan, R., Skinner, K.A.: LONER: LiDAR only neural representations for real-time slam. IEEE Robot. Autom. Lett. 8(12), 8042–8049 (2023). https://doi.org/10.1109/LRA.2023.3324521
Jang, E., Gu, S., Poole, B.: Categorical reparameterization with Gumbel-softmax. arXiv preprint arXiv:1611.01144 (2016)
Johari, M.M., Carta, C., Fleuret, F.: ESLAM: efficient dense slam system based on hybrid representation of signed distance fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 17408–17419 (2023)
Kwon, Y., Sung, M., Yoon, S.: Implicit LiDAR network: LiDAR super-resolution via interpolation weight prediction. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 8424–8430 (2022). https://doi.org/10.1109/ICRA46639.2022.9811992
Kühner, T., Kümmerle, J.: Large-scale volumetric scene reconstruction using LiDAR. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 6261–6267 (2020). https://doi.org/10.1109/ICRA40945.2020.9197388
Li, P., Shi, Y., Liu, T., Zhao, H., Zhou, G., Zhang, Y.Q.: Semi-supervised implicit scene completion from sparse LiDAR (2021)
Liu, L., Gu, J., Zaw Lin, K., Chua, T.S., Theobalt, C.: Neural sparse voxel fields. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 15651–15663. Curran Associates, Inc. (2020). https://proceedings.neurips.cc/paper_files/paper/2020/file/b4b758962f17808746e9bb832a6fa4b8-Paper.pdf
Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. SIGGRAPH Comput. Graph. 21(4), 163–169 (1987). https://doi.org/10.1145/37402.37422
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
Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. 41(4), 102:1–102:15 (2022). https://doi.org/10.1145/3528223.3530127
Newcombe, R.A., et al.: KinectFusion: real-time dense surface mapping and tracking. In: 2011 10th IEEE International Symposium on Mixed and Augmented Reality, pp. 127–136 (2011). https://doi.org/10.1109/ISMAR.2011.6092378
van den Oord, A., Vinyals, O., kavukcuoglu, K.: Neural discrete representation learning. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/7a98af17e63a0ac09ce2e96d03992fbc-Paper.pdf
Ortiz, J., Clegg, A., Dong, J., Sucar, E., Novotny, D., Zollhoefer, M., Mukadam, M.: iSDF: real-time neural signed distance fields for robot perception (2022)
Pan, Y., Kompis, Y., Bartolomei, L., Mascaro, R., Stachniss, C., Chli, M.: VoxField: non-projective signed distance fields for online planning and 3D reconstruction. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5331–5338 (2022). https://doi.org/10.1109/IROS47612.2022.9981318
Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Park, M., Son, H., Kim, E.: Implicit point function for lidar super-resolution in autonomous driving. IEEE Robot. Autom. Lett. 8(11), 7003–7009 (2023). https://doi.org/10.1109/LRA.2023.3313925
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
Ramezani, M., Wang, Y., Camurri, M., Wisth, D., Mattamala, M., Fallon, M.: The newer college dataset: handheld LiDAR, inertial and vision with ground truth. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4353–4360 (2020). https://doi.org/10.1109/IROS45743.2020.9340849
Rist, C.B., Emmerichs, D., Enzweiler, M., Gavrila, D.M.: Semantic scene completion using local deep implicit functions on lidar data. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 7205–7218 (2022). https://doi.org/10.1109/TPAMI.2021.3095302
Shi, C., Tang, F., Wu, Y., Jin, X., Ma, G.: Accurate implicit neural mapping with more compact representation in large-scale scenes using ranging data. IEEE Robot. Autom. Lett. 8(10), 6683–6690 (2023). https://doi.org/10.1109/LRA.2023.3311355
Sucar, E., Liu, S., Ortiz, J., Davison, A.J.: iMAP: implicit mapping and positioning in real-time. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6229–6238 (2021)
Takikawa, T., et al.: Variable bitrate neural fields. In: ACM SIGGRAPH 2022 Conference Proceedings. SIGGRAPH 2022, Association for Computing Machinery, New York (2022). https://doi.org/10.1145/3528233.3530727
Takikawa, T., et al.: Neural geometric level of detail: real-time rendering with implicit 3D shapes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11358–11367 (2021)
Vizzo, I., Chen, X., Chebrolu, N., Behley, J., Stachniss, C.: Poisson surface reconstruction for LiDAR odometry and mapping. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2021). http://www.ipb.uni-bonn.de/pdfs/vizzo2021icra.pdf
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: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol. 34, pp. 27171–27183. Curran Associates, Inc. (2021). https://proceedings.neurips.cc/paper_files/paper/2021/file/e41e164f7485ec4a28741a2d0ea41c74-Paper.pdf
Yan, Z., Tian, Y., Shi, X., Guo, P., Wang, P., Zha, H.: Continual neural mapping: learning an implicit scene representation from sequential observations. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 15782–15792 (2021)
Yan, Z., Yang, H., Zha, H.: Active neural mapping. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10981–10992 (2023)
Zhang, K., Riegler, G., Snavely, N., Koltun, V.: NeRF++: analyzing and improving neural radiance fields (2020)
Zhong, X., Pan, Y., Behley, J., Stachniss, C.: Shine-mapping: large-scale 3D mapping using sparse hierarchical implicit neural representations. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2023)
Zhu, Z., et al.: NICE-SLAM: neural implicit scalable encoding for SLAM. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12786–12796 (2022)
Acknowledgments
This work was supported by Korea Evaluation Institute Of Industrial Technology (KEIT) grant funded by the Korea government(MOTIE) (No. 20023455, Development of Cooperate Mapping, Environment Recognition and Autonomous Driving Technology for Multi Mobile Robots Operating in Large-scale Indoor Workspace).
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Park, M., Woo, S., Kim, E. (2025). Decomposition of Neural Discrete Representations for Large-Scale 3D Mapping. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15130. Springer, Cham. https://doi.org/10.1007/978-3-031-73220-1_21
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