Learning Neural Volumetric Field for Point Cloud Geometry Compression

• URL: http://arxiv.org/abs/2212.05589v1
• Date: Sun, 11 Dec 2022 19:55:24 GMT
• Title: Learning Neural Volumetric Field for Point Cloud Geometry Compression
• Authors: Yueyu Hu and Yao Wang
• Abstract summary: We propose to code the geometry of a given point cloud by learning a neural field. 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.
• Score: 13.691147541041804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

Related papers

• 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)
• Parametric Surface Constrained Upsampler Network for Point Cloud [33.033469444588086]
  We introduce a novel surface regularizer into the upsampler network by forcing the neural network to learn the underlying parametric surface represented by bicubic functions and rotation functions. These designs are integrated into two different networks for two tasks that take advantages of upsampling layers. The state-of-the-art experimental results on both tasks demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2023-03-14T21:12:54Z)
• 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)
• Point Cloud Semantic Segmentation using Multi Scale Sparse Convolution Neural Network [0.0]
  We propose a feature extraction module based on multi-scale ultra-sparse convolution and a feature selection module based on channel attention. By introducing multi-scale sparse convolution, network could capture richer feature information based on convolution kernels of different sizes.
  arXiv  Detail & Related papers  (2022-05-03T15:01:20Z)
• Differentiable Convolution Search for Point Cloud Processing [114.66038862207118]
  We propose a novel differential convolution search paradigm on point clouds. It can work in a purely data-driven manner and thus is capable of auto-creating a group of suitable convolutions for geometric shape modeling. We also propose a joint optimization framework for simultaneous search of internal convolution and external architecture, and introduce epsilon-greedy algorithm to alleviate the effect of discretization error.
  arXiv  Detail & Related papers  (2021-08-29T14:42:03Z)
• SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering [21.563862632172363]
  We propose a self-supervised point cloud upsampling network (SSPU-Net) to generate dense point clouds without using ground truth. To achieve this, we exploit the consistency between the input sparse point cloud and generated dense point cloud for the shapes and rendered images.
  arXiv  Detail & Related papers  (2021-08-01T13:26:01Z)
• ParaNet: Deep Regular Representation for 3D Point Clouds [62.81379889095186]
  ParaNet is a novel end-to-end deep learning framework for representing 3D point clouds. It converts an irregular 3D point cloud into a regular 2D color image, named point geometry image (PGI) In contrast to conventional regular representation modalities based on multi-view projection and voxelization, the proposed representation is differentiable and reversible.
  arXiv  Detail & Related papers  (2020-12-05T13:19:55Z)
• Learning-based lossless compression of 3D point cloud geometry [11.69103847045569]
  encoder operates in a hybrid mode, mixing octree and voxel-based coding. Our method outperforms the state-of-the-art MPEG G-PCC standard with average rate savings of 28%.
  arXiv  Detail & Related papers  (2020-11-30T11:27:16Z)
• Self-Sampling for Neural Point Cloud Consolidation [83.31236364265403]
  We introduce a novel technique for neural point cloud consolidation which learns from only the input point cloud. We repeatedly self-sample the input point cloud with global subsets that are used to train a deep neural network. We demonstrate the ability to consolidate point sets from a variety of shapes, while eliminating outliers and noise.
  arXiv  Detail & Related papers  (2020-08-14T17:16:02Z)
• GRNet: Gridding Residual Network for Dense Point Cloud Completion [54.43648460932248]
  Estimating the complete 3D point cloud from an incomplete one is a key problem in many vision and robotics applications. We propose a novel Gridding Residual Network (GRNet) for point cloud completion. Experimental results indicate that the proposed GRNet performs favorably against state-of-the-art methods on the ShapeNet, Completion3D, and KITTI benchmarks.
  arXiv  Detail & Related papers  (2020-06-06T02:46:39Z)
• Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds [145.79324955896845]
  We propose a permutable anisotropic convolutional operation (PAI-Conv) that calculates soft-permutation matrices for each point. Experiments on point clouds demonstrate that PAI-Conv produces competitive results in classification and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2020-05-27T02:42:29Z)

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.