In recent years, Neural Radiance Fields (NeRF) has revolutionized
three-dimensional (3D) reconstruction with its implicit representation.
Building upon NeRF, 3D Gaussian Splatting (3D-GS) has departed from the
implicit representation of neural networks and instead directly represents
scenes as point clouds with Gaussian-shaped distributions. While this shift has
notably elevated the rendering quality and speed of radiance fields but
inevitably led to a significant increase in memory usage. Additionally,
effectively rendering dynamic scenes in 3D-GS has emerged as a pressing
challenge. To address these concerns, this paper purposes a refined 3D Gaussian
representation for high-quality dynamic scene reconstruction. Firstly, we use a
deformable multi-layer perceptron (MLP) network to capture the dynamic offset
of Gaussian points and express the color features of points through hash
encoding and a tiny MLP to reduce storage requirements. Subsequently, we
introduce a learnable denoising mask coupled with denoising loss to eliminate
noise points from the scene, thereby further compressing 3D Gaussian model.
Finally, motion noise of points is mitigated through static constraints and
motion consistency constraints. Experimental results demonstrate that our
method surpasses existing approaches in rendering quality and speed, while
significantly reducing the memory usage associated with 3D-GS, making it highly
suitable for various tasks such as novel view synthesis, and dynamic mapping.

使用变形的多层感知器（MLP）网络捕捉动态偏移的高斯点并通过哈希编码和小型 MLP 来表示点的颜色特征，引入可学习的去噪掩模结合去噪损失以从场景中消除噪点，通过静态约束和运动一致性约束减轻点的运动噪声，实验证明我们的方法在渲染质量和速度上超过了现有方法，同时显著减少了与 3D-GS 相关的内存使用，非常适用于新的视角合成和动态建图等任务。

优化的三维高斯表示用于高质量动态场景重建

A Refined 3D Gaussian Representation for High-Quality Dynamic Scene  Reconstruction

We present a novel edge-level ego-network encoding for learning on graphs
that can boost Message Passing Graph Neural Networks (MP-GNNs) by providing
additional node and edge features or extending message-passing formats. The
proposed encoding is sufficient to distinguish Strongly Regular Graphs, a
family of challenging 3-WL equivalent graphs. We show theoretically that such
encoding is more expressive than node-based sub-graph MP-GNNs. In an empirical
evaluation on four benchmarks with 10 graph datasets, our results match or
improve previous baselines on expressivity, graph classification, graph
regression, and proximity tasks -- while reducing memory usage by 18.1x in
certain real-world settings.

通过提供额外的节点和边缘特征或扩展消息传递格式，我们提出了一种新颖的用于在图上学习的边缘级自我网络编码，可以提升消息传递图神经网络（MP-GNNs）的能力。理论上，该编码比基于节点的子图 MP-GNNs 更具表现力。在四个基准测试和 10 个图形数据集的实证评估中，我们的结果在表现力、图形分类、图形回归和接近性任务方面与之前的基准线相匹配或有所提高，同时在某些实际环境中减少了 18.1 倍的内存使用。

通过边级别自我网络编码改进子图 - GNNs

Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings

Traditional optimization methods rely on the use of single-precision floating
point arithmetic, which can be costly in terms of memory size and computing
power. However, mixed precision optimization techniques leverage the use of
both single and half-precision floating point arithmetic to reduce memory
requirements while maintaining model accuracy. We provide here an algorithm to
further reduce memory usage during the training of a model by getting rid of
the floating point copy of the parameters, virtually keeping only
half-precision numbers. We also explore the benefits of getting rid of the
gradient's value by executing the optimizer step during the back-propagation.
In practice, we achieve up to 25% lower peak memory use and 15% faster training
while maintaining the same level of accuracy.

传统优化方法依赖于使用单精度浮点算术，在内存大小和计算性能方面具有成本。然而，混合精度优化技术利用单精度和半精度浮点算术来降低内存需求，同时保持模型准确性。我们在训练模型期间提供了一种算法，通过摆脱参数的浮点副本，实际上只保留半精度数，进一步减少内存使用。我们还通过在反向传播期间执行优化器步骤来探索去除梯度值的好处。在实践中，我们实现了高达 25% 的峰值内存使用降低和 15% 的更快训练速度，同时保持相同水平的准确性。