Despite considerable progress being achieved in point cloud geometry
compression, there still remains a challenge in effectively compressing
large-scale scenes with sparse surfaces. Another key challenge lies in reducing
decoding latency, a crucial requirement in real-world application. In this
paper, we propose Pointsoup, an efficient learning-based geometry codec that
attains high-performance and extremely low-decoding-latency simultaneously.
Inspired by conventional Trisoup codec, a point model-based strategy is devised
to characterize local surfaces. Specifically, skin features are embedded from
local windows via an attention-based encoder, and dilated windows are
introduced as cross-scale priors to infer the distribution of quantized
features in parallel. During decoding, features undergo fast refinement,
followed by a folding-based point generator that reconstructs point coordinates
with fairly fast speed. Experiments show that Pointsoup achieves
state-of-the-art performance on multiple benchmarks with significantly lower
decoding complexity, i.e., up to 90$\sim$160$\times$ faster than the G-PCCv23
Trisoup decoder on a comparatively low-end platform (e.g., one RTX 2080Ti).
Furthermore, it offers variable-rate control with a single neural model
(2.9MB), which is attractive for industrial practitioners.

Pointsoup 是一种高性能、极低解码延迟的高效基于学习的几何编解码器，通过将皮肤特征通过基于注意力的编码器嵌入到本地窗口中并引入扩张窗口作为跨尺度先验来推断量化特征的分布，实现了快速的特征细化，然后使用基于折叠的点生成器以相当快的速度重构点坐标。实验证明，Pointsoup 在多个基准测试中达到了最先进的性能，且解码复杂度显著降低，即在较低端平台上（例如一块 RTX 2080Ti 上）与 G-PCCv23 Trisoup 解码器相比，快速了 90-160 倍。此外，它还提供变速控制，仅使用一个神经模型（2.9MB），对于工业实践者非常有吸引力。

Pointsoup：用于大规模点云场景的高性能和极低解码延迟的学习几何编解码器

Pointsoup: High-Performance and Extremely Low-Decoding-Latency Learned  Geometry Codec for Large-Scale Point Cloud Scenes

This study develops a unified Point Cloud Geometry (PCG) compression method
through the processing of multiscale sparse tensor-based voxelized PCG. We call
this compression method SparsePCGC. The proposed SparsePCGC is a low complexity
solution because it only performs the convolutions on sparsely-distributed
Most-Probable Positively-Occupied Voxels (MP-POV). The multiscale
representation also allows us to compress scale-wise MP-POVs by exploiting
cross-scale and same-scale correlations extensively and flexibly. The overall
compression efficiency highly depends on the accuracy of estimated occupancy
probability for each MP-POV. Thus, we first design the Sparse Convolution-based
Neural Network (SparseCNN) which stacks sparse convolutions and voxel sampling
to best characterize and embed spatial correlations. We then develop the
SparseCNN-based Occupancy Probability Approximation (SOPA) model to estimate
the occupancy probability either in a single-stage manner only using the
cross-scale correlation, or in a multi-stage manner by exploiting stage-wise
correlation among same-scale neighbors. Besides, we also suggest the SparseCNN
based Local Neighborhood Embedding (SLNE) to aggregate local variations as
spatial priors in feature attribute to improve the SOPA. Our unified approach
not only shows state-of-the-art performance in both lossless and lossy
compression modes across a variety of datasets including the dense object PCGs
(8iVFB, Owlii, MUVB) and sparse LiDAR PCGs (KITTI, Ford) when compared with
standardized MPEG G-PCC and other prevalent learning-based schemes, but also
has low complexity which is attractive to practical applications.

本研究提出了一种基于多尺度稀疏张量的点云几何压缩方法，称为 SparsePCGC，使用 Sparse Convolution-based Neural Network 和 Occupancy Probability Approximation 模型计算不同尺度的空间相关性来提高估计空间占用概率的准确性和压缩效率。在各种数据集中，该方法在无损和有损压缩模式下均表现出与标准化的 MPEG G-PCC 以及流行的基于学习的方案相比的最先进性能，同时具有低复杂度的优势。

基于稀疏张量的点云几何多尺度表示压缩

Sparse Tensor-based Multiscale Representation for Point Cloud Geometry  Compression

This paper presents a novel end-to-end Learned Point Cloud Geometry
Compression (a.k.a., Learned-PCGC) framework, to efficiently compress the point
cloud geometry (PCG) using deep neural networks (DNN) based variational
autoencoders (VAE). In our approach, PCG is first voxelized, scaled and
partitioned into non-overlapped 3D cubes, which is then fed into stacked 3D
convolutions for compact latent feature and hyperprior generation. Hyperpriors
are used to improve the conditional probability modeling of latent features. A
weighted binary cross-entropy (WBCE) loss is applied in training while an
adaptive thresholding is used in inference to remove unnecessary voxels and
reduce the distortion. Objectively, our method exceeds the geometry-based point
cloud compression (G-PCC) algorithm standardized by well-known Moving Picture
Experts Group (MPEG) with a significant performance margin, e.g., at least 60%
BD-Rate (Bjontegaard Delta Rate) gains, using common test datasets.
Subjectively, our method has presented better visual quality with smoother
surface reconstruction and appealing details, in comparison to all existing
MPEG standard compliant PCC methods. Our method requires about 2.5MB parameters
in total, which is a fairly small size for practical implementation, even on
embedded platform. Additional ablation studies analyze a variety of aspects
(e.g., cube size, kernels, etc) to explore the application potentials of our
learned-PCGC.

本文提出了一种基于神经网络的自动编码器和 3D 卷积的点云几何压缩方法，相对于 MPEG 标准压缩算法，可以获得至少 60% BD-Rate 增益，并在视觉效果上表现优越。