Neural Fields (NFs) have gained momentum as a tool for compressing various
data modalities - e.g. images and videos. This work leverages previous advances
and proposes a novel NF-based compression algorithm for 3D data. We derive two
versions of our approach - one tailored to watertight shapes based on Signed
Distance Fields (SDFs) and, more generally, one for arbitrary non-watertight
shapes using Unsigned Distance Fields (UDFs). We demonstrate that our method
excels at geometry compression on 3D point clouds as well as meshes. Moreover,
we show that, due to the NF formulation, it is straightforward to extend our
compression algorithm to compress both geometry and attribute (e.g. color) of
3D data.

本研究提出了一种基于神经场的三维数据压缩算法，能够优秀地进行几何压缩以及属性（例如颜色）压缩。

神经场的 3D 压缩

3D Compression Using Neural Fields

Point cloud compression (PCC) is a key enabler for various 3-D applications,
owing to the universality of the point cloud format. Ideally, 3D point clouds
endeavor to depict object/scene surfaces that are continuous. Practically, as a
set of discrete samples, point clouds are locally disconnected and sparsely
distributed. This sparse nature is hindering the discovery of local correlation
among points for compression. Motivated by an analysis with fractal dimension,
we propose a heterogeneous approach with deep learning for lossy point cloud
geometry compression. On top of a base layer compressing a coarse
representation of the input, an enhancement layer is designed to cope with the
challenging geometric residual/details. Specifically, a point-based network is
applied to convert the erratic local details to latent features residing on the
coarse point cloud. Then a sparse convolutional neural network operating on the
coarse point cloud is launched. It utilizes the continuity/smoothness of the
coarse geometry to compress the latent features as an enhancement bit-stream
that greatly benefits the reconstruction quality. When this bit-stream is
unavailable, e.g., due to packet loss, we support a skip mode with the same
architecture which generates geometric details from the coarse point cloud
directly. Experimentation on both dense and sparse point clouds demonstrate the
state-of-the-art compression performance achieved by our proposal. Our code is
available at this https URL

本文介绍了一种基于深度学习和卷积神经网络的、适用于点云的几何压缩方法，实现了对于点云的空间信息和几何细节的有损压缩，该方法可以有效地处理点云中的局部相关性，提高压缩和重建质量。

GRASP-Net: 基于几何残留分析与合成的点云压缩

GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud  Compression

We propose a practical deep generative approach for lossless point cloud
geometry compression, called MSVoxelDNN, and show that it significantly reduces
the rate compared to the MPEG G-PCC codec. Our previous work based on
autoregressive models (VoxelDNN) has a fast training phase, however, inference
is slow as the occupancy probabilities are predicted sequentially, voxel by
voxel. In this work, we employ a multiscale architecture which models voxel
occupancy in coarse-to-fine order. At each scale, MSVoxelDNN divides voxels
into eight conditionally independent groups, thus requiring a single network
evaluation per group instead of one per voxel. We evaluate the performance of
MSVoxelDNN on a set of point clouds from Microsoft Voxelized Upper Bodies
(MVUB) and MPEG, showing that the current method speeds up encoding/decoding
times significantly compared to the previous VoxelDNN, while having average
rate saving over G-PCC of 17.5%. The implementation is available at
this https URL

提出了一种名为 MSVoxelDNN 的实用深度生成方法，用于无损点云几何压缩，并表明它与 MPEG G-PCC 编解码器相比大大降低了比特率。

无损点云几何压缩的多尺度深度上下文建模

Multiscale deep context modeling for lossless point cloud geometry  compression

Efficient point cloud compression is fundamental to enable the deployment of
virtual and mixed reality applications, since the number of points to code can
range in the order of millions. In this paper, we present a novel data-driven
geometry compression method for static point clouds based on learned
convolutional transforms and uniform quantization. We perform joint
optimization of both rate and distortion using a trade-off parameter. In
addition, we cast the decoding process as a binary classification of the point
cloud occupancy map. Our method outperforms the MPEG reference solution in
terms of rate-distortion on the Microsoft Voxelized Upper Bodies dataset with
51.5% BDBR savings on average. Moreover, while octree-based methods face
exponential diminution of the number of points at low bitrates, our method
still produces high resolution outputs even at low bitrates. Code and
supplementary material are available at
this https URL .

本篇论文介绍了基于学习的卷积变换和均匀量化的静态点云数据几何压缩方法，并将解码过程视为点云占用图的二元分类。该方法在 Microsoft Voxelized Upper Bodies 数据集上表现优异，平均 BDBR 节省率达 51.5%，且能够在低比特率下仍产生高分辨率的输出。