Dynamic Point Cloud Compression with Cross-Sectional Approach
- URL: http://arxiv.org/abs/2204.11409v1
- Date: Mon, 25 Apr 2022 02:58:18 GMT
- Title: Dynamic Point Cloud Compression with Cross-Sectional Approach
- Authors: Faranak Tohidi, Manoranjan Paul, Anwaar Ulhaq
- Abstract summary: MPEG finalized a Video-based Point Cloud Compression standard known as V-PCC.
The proposed method addresses these limitations by using a novel cross-sectional approach.
The experimental results using standard video sequences show that the proposed technique can achieve better compression in both geometric and texture data.
- Score: 10.850101961203748
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The recent development of dynamic point clouds has introduced the possibility
of mimicking natural reality, and greatly assisting quality of life. However,
to broadcast successfully, the dynamic point clouds require higher compression
due to their huge volume of data compared to the traditional video. Recently,
MPEG finalized a Video-based Point Cloud Compression standard known as V-PCC.
However, V-PCC requires huge computational time due to expensive normal
calculation and segmentation, sacrifices some points to limit the number of 2D
patches, and cannot occupy all spaces in the 2D frame. The proposed method
addresses these limitations by using a novel cross-sectional approach. This
approach reduces expensive normal estimation and segmentation, retains more
points, and utilizes more spaces for 2D frame generation compared to the VPCC.
The experimental results using standard video sequences show that the proposed
technique can achieve better compression in both geometric and texture data
compared to the V-PCC standard.
Related papers
- Color Enhancement for V-PCC Compressed Point Cloud via 2D Attribute Map Optimization [8.21390074063036]
Video-based point cloud compression (V-PCC) converts the dynamic point cloud data into video sequences.
This paper introduces a framework designed to enhance the color quality in the V-PCC compressed point clouds.
arXiv Detail & Related papers (2024-12-19T01:58:00Z) - Implicit Neural Compression of Point Clouds [58.45774938982386]
NeRC$textbf3$ is a novel point cloud compression framework leveraging implicit neural representations to handle both geometry and attributes.
For dynamic point clouds, 4D-NeRC$textbf3$ demonstrates superior geometry compression compared to state-of-the-art G-PCC and V-PCC standards.
arXiv Detail & Related papers (2024-12-11T03:22:00Z) - Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer [52.40992954884257]
3D visualization techniques have fundamentally transformed how we interact with digital content.
Massive data size of point clouds presents significant challenges in data compression.
We propose an end-to-end deep learning framework that seamlessly integrates PCAC with differentiable rendering.
arXiv Detail & Related papers (2024-11-12T16:12:51Z) - Improved Video VAE for Latent Video Diffusion Model [55.818110540710215]
Video Autoencoder (VAE) aims to compress pixel data into low-dimensional latent space, playing an important role in OpenAI's Sora.
Most of existing VAEs inflate a pretrained image VAE into the 3D causal structure for temporal-spatial compression.
We propose a new KTC architecture and a group causal convolution (GCConv) module to further improve video VAE (IV-VAE)
arXiv Detail & Related papers (2024-11-10T12:43:38Z) - The JPEG Pleno Learning-based Point Cloud Coding Standard: Serving Man and Machine [49.16996486119006]
Deep learning has emerged as a powerful tool in point cloud coding.
JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding standard.
This paper provides a complete technical description of the JPEG PCC standard.
arXiv Detail & Related papers (2024-09-12T15:20:23Z) - Hierarchical Prior-based Super Resolution for Point Cloud Geometry
Compression [39.052583172727324]
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.
arXiv Detail & Related papers (2024-02-17T11:15:38Z) - 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) - Inter-Frame Compression for Dynamic Point Cloud Geometry Coding [14.79613731546357]
We propose a lossy compression scheme that predicts the latent representation of the current frame using the previous frame.
The proposed network utilizes convolutions with hierarchical multiscale 3D feature learning to encode the current frame.
The proposed method achieves more than 88% BD-Rate (Bjontegaard Delta Rate) reduction against G-PCCv20 Octree.
arXiv Detail & Related papers (2022-07-25T22:17:19Z) - 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) - Efficient VVC Intra Prediction Based on Deep Feature Fusion and
Probability Estimation [57.66773945887832]
We propose to optimize Versatile Video Coding (VVC) complexity at intra-frame prediction, with a two-stage framework of deep feature fusion and probability estimation.
Experimental results on standard database demonstrate the superiority of proposed method, especially for High Definition (HD) and Ultra-HD (UHD) video sequences.
arXiv Detail & Related papers (2022-05-07T08:01:32Z) - 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.