Abstract
We present a novel octree-based multi-level framework for large-scale point cloud compression, which can organize sparse and unstructured point clouds in a memory-efficient way. In this framework, we propose a new entropy model that explores the hierarchical dependency in an octree using the context of siblings’ children, ancestors, and neighbors to encode the occupancy information of each non-leaf octree node into a bitstream. Moreover, we locally fit quadratic surfaces with a voxel-based geometry-aware module to provide geometric priors in entropy encoding. These strong priors empower our entropy framework to encode the octree into a more compact bitstream. In the decoding stage, we apply a two-step heuristic strategy to restore point clouds with better reconstruction quality. The quantitative evaluation shows that our method outperforms state-of-the-art baselines with a bitrate improvement of 11–16% and 12–14% on the KITTI Odometry and nuScenes datasets, respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahn, J.K., Lee, K.Y., Sim, J.Y., Kim, C.S.: Large-scale 3D point cloud compression using adaptive radial distance prediction in hybrid coordinate domains. JSTSP 9(3), 422–434 (2014)
Biswas, S., Liu, J., Wong, K., Wang, S., Urtasun, R.: MuSCLE: multi sweep compression of lidar using deep entropy models. In: NIPS (2020)
Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: CVPR (2020)
Devillers, O., Gandoin, P.M.: Geometric compression for interactive transmission. In: VIS 2000 (Cat. No. 00CH37145) (2000)
Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3d object reconstruction from a single image. In: CVPR (2017)
Fu, C., Li, G., Song, R., Gao, W., Liu, S.: OctAttention: octree-based large-scale contexts model for point cloud compression. In: AAAI (2022)
Garcia, D.C., de Queiroz, R.L.: Intra-frame context-based octree coding for point-cloud geometry. In: ICIP (2018)
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: CVPR (2012)
Google: Draco (2017). https://github.com/google/draco
Graziosi, D., Nakagami, O., Kuma, S., Zaghetto, A., Suzuki, T., Tabatabai, A.: An overview of ongoing point cloud compression standardization activities: video-based (V-PCC) and geometry-based (G-PCC). APSIPA Trans. Signal Inform. Process. 9, E13 (2020)
Huang, L., Wang, S., Wong, K., Liu, J., Urtasun, R.: OctSqueeze: octree-structured entropy model for lidar compression. In: CVPR (2020)
Huang, T., Liu, Y.: 3D point cloud geometry compression on deep learning. In: MM (2019)
Huang, Y., Peng, J., Kuo, C.C.J., Gopi, M.: A generic scheme for progressive point cloud coding. TVCG 14(2), 440–453 (2008)
Jackins, C.L., Tanimoto, S.L.: Oct-trees and their use in representing three-dimensional objects. CGIP 14(3), 249–270 (1980)
Kammerl, J., Blodow, N., Rusu, R.B., Gedikli, S., Beetz, M., Steinbach, E.: Real-time compression of point cloud streams. In: ICRA (2012)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)
Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: PointPillars: fast encoders for object detection from point clouds. In: CVPR (2019)
Meagher, D.: Geometric modeling using octree encoding. CGIP 19(2), 129–147 (1982)
Mekuria, R., Laserre, S., Tulvan, C.: Performance assessment of point cloud compression. In: VCIP (2017)
Nenci, F., Spinello, L., Stachniss, C.: Effective compression of range data streams for remote robot operations using h. 264. In: IROS (2014)
Nguyen, D.T., Quach, M., Valenzise, G., Duhamel, P.: Multiscale deep context modeling for lossless point cloud geometry compression. In: ICMEW (2021)
Qi, C., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: NIPS (2017)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: CVPR (2017)
Que, Z., Lu, G., Xu, D.: VoxelContext-Net: an octree based framework for point cloud compression. In: CVPR (2021)
Schnabel, R., Klein, R.: Octree-based point-cloud compression. In: PBG@ SIGGRAPH (2006)
Schwarz, S., et al.: Emerging MPEG standards for point cloud compression. JETCAS 9(1), 133–148 (2018)
Sun, X., Ma, H., Sun, Y., Liu, M.: A novel point cloud compression algorithm based on clustering. RA-L 4(2), 2132–2139 (2019)
Sun, X., Wang, S., Liu, M.: A novel coding architecture for multi-line lidar point clouds based on clustering and convolutional LSTM network. T-ITS 29(3), 2190–2201 (2020)
Theis, L., Shi, W., Cunningham, A., Huszár, F.: Lossy image compression with compressive autoencoders. In: 5th International Conference on Learning Representations, ICLR (2017)
Tian, D., Ochimizu, H., Feng, C., Cohen, R., Vetro, A.: Geometric distortion metrics for point cloud compression. In: ICIP (2017)
Tu, C., Takeuchi, E., Carballo, A., Takeda, K.: Point cloud compression for 3D lidar sensor using recurrent neural network with residual blocks. In: ICRA (2019)
Wang, J., Zhu, H., Ma, Z., Chen, T., Liu, H., Shen, Q.: Learned point cloud geometry compression. CoRR abs/1909.12037 (2019)
Wang, S., Jiao, J., Cai, P., Liu, M.: R-PCC: a baseline for range image-based point cloud compression. CoRR abs/2109.07717 (2021)
Wiesmann, L., Milioto, A., Chen, X., Stachniss, C., Behley, J.: Deep compression for dense point cloud maps. RA-L 6(2), 2060–2067 (2021)
Yan, W., Shao, Y., Liu, S., Li, T.H., Li, Z., Li, G.: Deep autoencoder-based lossy geometry compression for point clouds. CoRR abs/1905.03691 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, Z., Qian, Z., Wang, S., Chen, Q. (2022). Point Cloud Compression with Sibling Context and Surface Priors. 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 13698. Springer, Cham. https://doi.org/10.1007/978-3-031-19839-7_43
Download citation
DOI: https://doi.org/10.1007/978-3-031-19839-7_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19838-0
Online ISBN: 978-3-031-19839-7
eBook Packages: Computer ScienceComputer Science (R0)