Learning Neural Volumetric Field for Point Cloud Geometry Compression

Yueyu Hu, Yao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Due to the diverse sparsity, high dimensionality, and large temporal variation of dynamic point clouds, it remains a challenge to design an efficient point cloud compression method. We propose to code the geometry of a given point cloud by learning a neural volumetric field. Instead of representing the entire point cloud using a single overfit network, we divide the entire space into small cubes and represent each non-empty cube by a neural network and an input latent code. The network is shared among all the cubes in a single frame or multiple frames, to exploit the spatial and temporal redundancy. The neural field representation of the point cloud includes the network parameters and all the latent codes, which are generated by using back-propagation over the network parameters and its input. By considering the entropy of the network parameters and the latent codes as well as the distortion between the original and reconstructed cubes in the loss function, we derive a rate-distortion (R-D) optimal representation. Experimental results show that the proposed coding scheme achieves superior R-D performances compared to the octree-based G-PCC, especially when applied to multiple frames of a point cloud video. The code is available at https://github.com/huzi96/NVFPCC/.

Original languageEnglish (US)
Title of host publication2022 Picture Coding Symposium, PCS 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781665492577
StatePublished - 2022
Event2022 Picture Coding Symposium, PCS 2022 - San Jose, United States
Duration: Dec 7 2022Dec 9 2022

Publication series

Name2022 Picture Coding Symposium, PCS 2022 - Proceedings


Conference2022 Picture Coding Symposium, PCS 2022
Country/TerritoryUnited States
CitySan Jose


  • Neural Field
  • Point Cloud Compression
  • Rate-Distortion optimization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Signal Processing


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