Hierarchical Prior-based Super Resolution for Point Cloud Geometry Compression

• URL: http://arxiv.org/abs/2402.11250v1
• Date: Sat, 17 Feb 2024 11:15:38 GMT
• Title: Hierarchical Prior-based Super Resolution for Point Cloud Geometry Compression
• Authors: Dingquan Li and Kede Ma and Jing Wang and Ge Li
• Abstract summary: The Geometry-based Point Cloud Compression (G-PCC) has been developed by the Moving Picture Experts Group to compress point clouds. This paper proposes a hierarchical prior-based super resolution method for point cloud geometry compression.
• Score: 39.052583172727324
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Geometry-based Point Cloud Compression (G-PCC) has been developed by the Moving Picture Experts Group to compress point clouds. In its lossy mode, the reconstructed point cloud by G-PCC often suffers from noticeable distortions due to the na\"{i}ve geometry quantization (i.e., grid downsampling). This paper proposes a hierarchical prior-based super resolution method for point cloud geometry compression. The content-dependent hierarchical prior is constructed at the encoder side, which enables coarse-to-fine super resolution of the point cloud geometry at the decoder side. A more accurate prior generally yields improved reconstruction performance, at the cost of increased bits required to encode this side information. With a proper balance between prior accuracy and bit consumption, the proposed method demonstrates substantial Bjontegaard-delta bitrate savings on the MPEG Cat1A dataset, surpassing the octree-based and trisoup-based G-PCC v14. We provide our implementations for reproducible research at https://github.com/lidq92/mpeg-pcc-tmc13.

Related papers

• Point Cloud Compression with Bits-back Coding [32.9521748764196]
  This paper specializes in using a deep learning-based probabilistic model to estimate the Shannon's entropy of the point cloud information. Once the entropy of the point cloud dataset is estimated, we use the learned CVAE model to compress the geometric attributes of the point clouds. The novelty of our method with bits-back coding specializes in utilizing the learned latent variable model of the CVAE to compress the point cloud data.
  arXiv  Detail & Related papers  (2024-10-09T06:34:48Z)
• SPAC: Sampling-based Progressive Attribute Compression for Dense Point Clouds [51.313922535437726]
  We propose an end-to-end compression method for dense point clouds. The proposed method combines a frequency sampling module, an adaptive scale feature extraction module with geometry assistance, and a global hyperprior entropy model.
  arXiv  Detail & Related papers  (2024-09-16T13:59:43Z)
• Lightweight super resolution network for point cloud geometry compression [34.42460388539782]
  We present an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network. The proposed method involves decomposing a point cloud into a base point cloud and the patterns for reconstructing the original point cloud. Experiments on MPEG Cat1 (Solid) and Cat2 datasets demonstrate the remarkable compression performance achieved by our method.
  arXiv  Detail & Related papers  (2023-11-02T03:34:51Z)
• Geometric Prior Based Deep Human Point Cloud Geometry Compression [67.49785946369055]
  We leverage the human geometric prior in geometry redundancy removal of point clouds. We can envisage high-resolution human point clouds as a combination of geometric priors and structural deviations. The proposed framework can operate in a play-and-plug fashion with existing learning based point cloud compression methods.
  arXiv  Detail & Related papers  (2023-05-02T10:35:20Z)
• GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression [16.98171403698783]
  We propose a heterogeneous approach with deep learning for lossy point cloud geometry compression. Specifically, a point-based network is applied to convert the erratic local details to latent features residing on the coarse point cloud.
  arXiv  Detail & Related papers  (2022-09-09T17:09:02Z)
• Efficient dynamic point cloud coding using Slice-Wise Segmentation [10.850101961203748]
  MPEG recently developed a video-based point cloud compression (V-PCC) standard for dynamic point cloud coding. Patch generations and self-occluded points in the 3D to the 2D projection are the main reasons for missing data using V-PCC. This paper proposes a new method that introduces overlapping slicing to decrease the number of patches generated and the amount of data lost.
  arXiv  Detail & Related papers  (2022-08-17T04:23:45Z)
• Learned Video Compression via Heterogeneous Deformable Compensation Network [78.72508633457392]
  We propose a learned video compression framework via heterogeneous deformable compensation strategy (HDCVC) to tackle the problems of unstable compression performance. More specifically, the proposed algorithm extracts features from the two adjacent frames to estimate content-Neighborhood heterogeneous deformable (HetDeform) kernel offsets. Experimental results indicate that HDCVC achieves superior performance than the recent state-of-the-art learned video compression approaches.
  arXiv  Detail & Related papers  (2022-07-11T02:31:31Z)
• SoftPool++: An Encoder-Decoder Network for Point Cloud Completion [93.54286830844134]
  We propose a novel convolutional operator for the task of point cloud completion. The proposed operator does not require any max-pooling or voxelization operation. We show that our approach achieves state-of-the-art performance in shape completion at low and high resolutions.
  arXiv  Detail & Related papers  (2022-05-08T15:31:36Z)
• Point Cloud Compression with Sibling Context and Surface Priors [47.96018990521301]
  We present a novel octree-based multi-level framework for large-scale point cloud compression. In this framework, we propose a new entropy model that explores the hierarchical dependency in an octree. We locally fit surfaces with a voxel-based geometry-aware module to provide geometric priors in entropy encoding.
  arXiv  Detail & Related papers  (2022-05-02T09:13:26Z)
• Multiscale Point Cloud Geometry Compression [29.605320327889142]
  We propose a multiscale-to-end learning framework which hierarchically reconstructs the 3D Point Cloud Geometry. The framework is developed on top of a sparse convolution based autoencoder for point cloud compression and reconstruction.
  arXiv  Detail & Related papers  (2020-11-07T16:11:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.