Dynamic Point Cloud Geometry Compression Using Multiscale Inter Conditional Coding

• URL: http://arxiv.org/abs/2301.12165v1
• Date: Sat, 28 Jan 2023 11:34:06 GMT
• Title: Dynamic Point Cloud Geometry Compression Using Multiscale Inter Conditional Coding
• Authors: Jianqiang Wang, Dandan Ding, Hao Chen, Zhan Ma
• Abstract summary: This work extends the Multiscale Sparse Representation (MSR) framework developed for Point Cloud Geometry Compression (PCGC) to support the dynamic PCGC. The reconstruction of the preceding Point Cloud Geometry (PCG) frame is progressively downscaled to generate multiscale temporal priors.
• Score: 27.013814232906817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work extends the Multiscale Sparse Representation (MSR) framework developed for static Point Cloud Geometry Compression (PCGC) to support the dynamic PCGC through the use of multiscale inter conditional coding. To this end, the reconstruction of the preceding Point Cloud Geometry (PCG) frame is progressively downscaled to generate multiscale temporal priors which are then scale-wise transferred and integrated with lower-scale spatial priors from the same frame to form the contextual information to improve occupancy probability approximation when processing the current PCG frame from one scale to another. Following the Common Test Conditions (CTC) defined in the standardization committee, the proposed method presents State-Of-The-Art (SOTA) compression performance, yielding 78% lossy BD-Rate gain to the latest standard-compliant V-PCC and 45% lossless bitrate reduction to the latest G-PCC. Even for recently-emerged learning-based solutions, our method still shows significant performance gains.

Related papers

• Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaption [57.056311855630916]
  We propose a Controllable Generative Image Compression framework, Control-GIC. It is capable of fine-grained adaption across a broad spectrum while ensuring high-fidelity and generality compression. We develop a conditional conditionalization that can trace back to historic encoded multi-granularity representations.
  arXiv  Detail & Related papers  (2024-06-02T14:22:09Z)
• 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)
• 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)
• Cross Modal Compression: Towards Human-comprehensible Semantic Compression [73.89616626853913]
  Cross modal compression is a semantic compression framework for visual data. We show that our proposed CMC can achieve encouraging reconstructed results with an ultrahigh compression ratio.
  arXiv  Detail & Related papers  (2022-09-06T15:31:11Z)
• 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)
• 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)
• Dynamic Point Cloud Compression with Cross-Sectional Approach [10.850101961203748]
  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.
  arXiv  Detail & Related papers  (2022-04-25T02:58:18Z)
• Communication-Efficient Federated Learning via Quantized Compressed Sensing [82.10695943017907]
  The presented framework consists of gradient compression for wireless devices and gradient reconstruction for a parameter server. Thanks to gradient sparsification and quantization, our strategy can achieve a higher compression ratio than one-bit gradient compression. We demonstrate that the framework achieves almost identical performance with the case that performs no compression.
  arXiv  Detail & Related papers  (2021-11-30T02:13:54Z)
• Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression [18.24902526033056]
  We develop a unified Point Cloud Geometry (PCG) compression method through Sparse Processing (STP) based multiscale representation of voxelized PCG. Applying the complexity reduces the complexity significantly because it only performs the convolutions centered at Most-Probable Positively-Occupied Voxels (MP-POV) The proposed method presents lightweight complexity due to point-wise, and tiny storage desire because of model sharing across all scales.
  arXiv  Detail & Related papers  (2021-11-20T17:02:45Z)
• 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.