The past several years have witnessed the emergence of learned point cloud
compression (PCC) techniques. However, current learning-based lossless point
cloud attribute compression (PCAC) methods either suffer from high
computational complexity or deteriorated compression performance. Moreover, the
significant variations in point cloud scale and sparsity encountered in
real-world applications make developing an all-in-one neural model a
challenging task. In this paper, we propose PoLoPCAC, an efficient and generic
lossless PCAC method that achieves high compression efficiency and strong
generalizability simultaneously. We formulate lossless PCAC as the task of
inferring explicit distributions of attributes from group-wise autoregressive
priors. A progressive random grouping strategy is first devised to efficiently
resolve the point cloud into groups, and then the attributes of each group are
modeled sequentially from accumulated antecedents. A locality-aware attention
mechanism is utilized to exploit prior knowledge from context windows in
parallel. Since our method directly operates on points, it can naturally avoids
distortion caused by voxelization, and can be executed on point clouds with
arbitrary scale and density. Experiments show that our method can be instantly
deployed once trained on a Synthetic 2k-ShapeNet dataset while enjoying
continuous bit-rate reduction over the latest G-PCCv23 on various datasets
(ShapeNet, ScanNet, MVUB, 8iVFB). Meanwhile, our method reports shorter coding
time than G-PCCv23 on the majority of sequences with a lightweight model size
(2.6MB), which is highly attractive for practical applications. Dataset, code
and trained model are available at
this https URL

在这篇论文中，我们提出了 PoLoPCAC，一种高效且通用的无损点云属性压缩方法，它在高压缩效率和强大的泛化能力方面取得了平衡。我们通过将无损 PCAC 建模为从群组先验中推断属性的显式分布的任务来实现压缩。我们的方法直接在点上操作，可以自然地避免由于体素化而引起的扭曲，并且可以在任意尺度和密度的点云上执行。实验证明，我们的方法在各种数据集上（ShapeNet、ScanNet、MVUB、8iVFB）比最新的 G-PCCv23 具有持续的比特率降低，并且比 G-PCCv23 在大多数序列上具有更短的编码时间和轻量级模型尺寸（2.6MB），这对于实际应用非常有吸引力。

高效通用的点云属性无损压缩的点模型

Efficient and Generic Point Model for Lossless Point Cloud Attribute  Compression

The emerging Learned Compression (LC) replaces the traditional codec modules
with Deep Neural Networks (DNN), which are trained end-to-end for
rate-distortion performance. This approach is considered as the future of
image/video compression, and major efforts have been dedicated to improving its
compression efficiency. However, most proposed works target compression
efficiency by employing more complex DNNS, which contributes to higher
computational complexity. Alternatively, this paper proposes to improve
compression by fully exploiting the existing DNN capacity. To do so, the latent
features are guided to learn a richer and more diverse set of features, which
corresponds to better reconstruction. A channel-wise feature decorrelation loss
is designed and is integrated into the LC optimization. Three strategies are
proposed and evaluated, which optimize (1) the transformation network, (2) the
context model, and (3) both networks. Experimental results on two established
LC methods show that the proposed method improves the compression with a
BD-Rate of up to 8.06%, with no added complexity. The proposed solution can be
applied as a plug-and-play solution to optimize any similar LC method.

通过充分利用现有的深度神经网络容量，本研究提出了一种改进压缩的方法，通过引导潜在特征学习更丰富和多样化的特征来实现更好的重构效果，并将通道相关的特征去相关损失集成到优化过程中，实验证明该方法在两种传统压缩方法上提高了压缩性能，没有增加额外的复杂性，可以作为类似方法的即插即用的优化方案。

增强学习图像压缩的通道特征去相关

Channel-wise Feature Decorrelation for Enhanced Learned Image  Compression

We introduce a lightweight, flexible and end-to-end trainable probability
density model parameterized by a constrained Fourier basis. We assess its
performance at approximating a range of multi-modal 1D densities, which are
generally difficult to fit. In comparison to the deep factorized model
introduced in [1], our model achieves a lower cross entropy at a similar
computational budget. In addition, we also evaluate our method on a toy
compression task, demonstrating its utility in learned compression.

我们介绍了一种轻量级、灵活和端到端可训练的概率密度模型，其参数由受限的傅里叶基函数表示。我们评估了该模型在逼近多模态一维密度方面的性能，这通常是难以拟合的。与 [1] 中引入的深度分解模型相比，我们的模型在类似的计算预算下实现了较低的交叉熵。此外，我们还通过一个玩具压缩任务对我们的方法进行了评估，展示了它在学习压缩中的实用性。