IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression

• URL: http://arxiv.org/abs/2208.02519v1
• Date: Thu, 4 Aug 2022 08:12:35 GMT
• Title: IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression
• Authors: Kang You, Pan Gao and Qing Li
• Abstract summary: We propose a set of significant improvements to patch-based point cloud compression. Experiments show that the improved patch-based autoencoder outperforms the state-of-the-art in terms of rate-distortion performance.
• Score: 11.410441760314564
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Point cloud is a crucial representation of 3D contents, which has been widely used in many areas such as virtual reality, mixed reality, autonomous driving, etc. With the boost of the number of points in the data, how to efficiently compress point cloud becomes a challenging problem. In this paper, we propose a set of significant improvements to patch-based point cloud compression, i.e., a learnable context model for entropy coding, octree coding for sampling centroid points, and an integrated compression and training process. In addition, we propose an adversarial network to improve the uniformity of points during reconstruction. Our experiments show that the improved patch-based autoencoder outperforms the state-of-the-art in terms of rate-distortion performance, on both sparse and large-scale point clouds. More importantly, our method can maintain a short compression time while ensuring the reconstruction quality.

Related papers

• 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)
• 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)
• Point Cloud Compression with Implicit Neural Representations: A Unified Framework [54.119415852585306]
  We present a pioneering point cloud compression framework capable of handling both geometry and attribute components. Our framework utilizes two coordinate-based neural networks to implicitly represent a voxelized point cloud. Our method exhibits high universality when contrasted with existing learning-based techniques.
  arXiv  Detail & Related papers  (2024-05-19T09:19:40Z)
• PIVOT-Net: Heterogeneous Point-Voxel-Tree-based Framework for Point Cloud Compression [8.778300313732027]
  We propose a heterogeneous point cloud compression (PCC) framework. We unify typical point cloud representations -- point-based, voxel-based, and tree-based representations -- and their associated backbones. We augment the framework with a proposed context-aware upsampling for decoding and an enhanced voxel transformer for feature aggregation.
  arXiv  Detail & Related papers  (2024-02-11T16:57:08Z)
• 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)
• Density-preserving Deep Point Cloud Compression [72.0703956923403]
  We propose a novel deep point cloud compression method that preserves local density information. Our method works in an auto-encoder fashion: the encoder downsamples the points and learns point-wise features, while the decoder upsamples the points using these features.
  arXiv  Detail & Related papers  (2022-04-27T03:42:15Z)
• Variable Rate Compression for Raw 3D Point Clouds [5.107705550575662]
  We propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. Our network is capable of explicitly processing point clouds and generating a compressed description.
  arXiv  Detail & Related papers  (2022-02-28T15:15:39Z)
• Deep Point Cloud Reconstruction [74.694733918351]
  Point cloud obtained from 3D scanning is often sparse, noisy, and irregular. To cope with these issues, recent studies have been separately conducted to densify, denoise, and complete inaccurate point cloud. We propose a deep point cloud reconstruction network consisting of two stages: 1) a 3D sparse stacked-hourglass network as for the initial densification and denoising, 2) a refinement via transformers converting the discrete voxels into 3D points.
  arXiv  Detail & Related papers  (2021-11-23T07:53:28Z)
• Patch-Based Deep Autoencoder for Point Cloud Geometry Compression [8.44208490359453]
  We propose a patch-based compression process using deep learning. We divide the point cloud into patches and compress each patch independently. In the decoding process, we finally assemble the decompressed patches into a complete point cloud.
  arXiv  Detail & Related papers  (2021-10-18T08:59:57Z)
• PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers [81.71904691925428]
  We present a new method that reformulates point cloud completion as a set-to-set translation problem. We also design a new model, called PoinTr, that adopts a transformer encoder-decoder architecture for point cloud completion. Our method outperforms state-of-the-art methods by a large margin on both the new benchmarks and the existing ones.
  arXiv  Detail & Related papers  (2021-08-19T17:58:56Z)

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.