This paper studies the problem of estimating physical properties (system
identification) through visual observations. To facilitate geometry-aware
guidance in physical property estimation, we introduce a novel hybrid framework
that leverages 3D Gaussian representation to not only capture explicit shapes
but also enable the simulated continuum to deduce implicit shapes during
training. We propose a new dynamic 3D Gaussian framework based on motion
factorization to recover the object as 3D Gaussian point sets across different
time states. Furthermore, we develop a coarse-to-fine filling strategy to
generate the density fields of the object from the Gaussian reconstruction,
allowing for the extraction of object continuums along with their surfaces and
the integration of Gaussian attributes into these continuums. In addition to
the extracted object surfaces, the Gaussian-informed continuum also enables the
rendering of object masks during simulations, serving as implicit shape
guidance for physical property estimation. Extensive experimental evaluations
demonstrate that our pipeline achieves state-of-the-art performance across
multiple benchmarks and metrics. Additionally, we illustrate the effectiveness
of the proposed method through real-world demonstrations, showcasing its
practical utility. Our project page is at this https URL

通过视觉观察来估计物理特性 (系统辨识) 的问题，本论文介绍了一种新颖的混合框架，该框架利用三维高斯表示不仅能捕捉明确的形状，还能使模拟连续推导训练中的隐含形状。我们提出了一种基于运动分解的新型动态三维高斯框架，以在不同时间状态下恢复物体作为三维高斯点集。此外，我们开发了一种由粗到细的填充策略，从高斯重建生成物体的密度场，允许提取物体连续及其表面，并将高斯属性融入这些连续体中。除提取的物体表面外，高斯信息的连续体还能在模拟中渲染物体掩膜，为物理特性估计提供隐含形状引导。大量实验评估表明，我们的管线在多个基准和度量标准上达到了最先进的性能。另外，我们通过真实世界演示展示了所提方法的实用性。

基于高斯信息的连续方法用于物性识别与模拟

Gaussian-Informed Continuum for Physical Property Identification and  Simulation

We present MVSGaussian, a new generalizable 3D Gaussian representation
approach derived from Multi-View Stereo (MVS) that can efficiently reconstruct
unseen scenes. Specifically, 1) we leverage MVS to encode geometry-aware
Gaussian representations and decode them into Gaussian parameters. 2) To
further enhance performance, we propose a hybrid Gaussian rendering that
integrates an efficient volume rendering design for novel view synthesis. 3) To
support fast fine-tuning for specific scenes, we introduce a multi-view
geometric consistent aggregation strategy to effectively aggregate the point
clouds generated by the generalizable model, serving as the initialization for
per-scene optimization. Compared with previous generalizable NeRF-based
methods, which typically require minutes of fine-tuning and seconds of
rendering per image, MVSGaussian achieves real-time rendering with better
synthesis quality for each scene. Compared with the vanilla 3D-GS, MVSGaussian
achieves better view synthesis with less training computational cost. Extensive
experiments on DTU, Real Forward-facing, NeRF Synthetic, and Tanks and Temples
datasets validate that MVSGaussian attains state-of-the-art performance with
convincing generalizability, real-time rendering speed, and fast per-scene
optimization.

MVSGaussian 是一种新的通用化的三维高斯表示方法，通过多视点立体视觉 (MVS) 有效重建未见过的场景，具有实时渲染速度和良好的综合质量。

快速通用高斯投影立体视图重建

Fast Generalizable Gaussian Splatting Reconstruction from Multi-View  Stereo

We propose NEDS-SLAM, an Explicit Dense semantic SLAM system based on 3D
Gaussian representation, that enables robust 3D semantic mapping, accurate
camera tracking, and high-quality rendering in real-time. In the system, we
propose a Spatially Consistent Feature Fusion model to reduce the effect of
erroneous estimates from pre-trained segmentation head on semantic
reconstruction, achieving robust 3D semantic Gaussian mapping. Additionally, we
employ a lightweight encoder-decoder to compress the high-dimensional semantic
features into a compact 3D Gaussian representation, mitigating the burden of
excessive memory consumption. Furthermore, we leverage the advantage of 3D
Gaussian splatting, which enables efficient and differentiable novel view
rendering, and propose a Virtual Camera View Pruning method to eliminate
outlier GS points, thereby effectively enhancing the quality of scene
representations. Our NEDS-SLAM method demonstrates competitive performance over
existing dense semantic SLAM methods in terms of mapping and tracking accuracy
on Replica and ScanNet datasets, while also showing excellent capabilities in
3D dense semantic mapping.

NEDS-SLAM 是一种基于 3D 高斯表示的显式稠密语义 SLAM 系统，可以实时进行鲁棒的 3D 语义映射、准确的相机跟踪和高质量的渲染。

NEDS-SLAM：一种新颖的神经显式稠密语义 SLAM 框架，使用 3D 高斯光滑插值

NEDS-SLAM: A Novel Neural Explicit Dense Semantic SLAM Framework using  3D Gaussian Splatting

In this paper, we introduce $\textbf{GS-SLAM}$ that first utilizes 3D
Gaussian representation in the Simultaneous Localization and Mapping (SLAM)
system. It facilitates a better balance between efficiency and accuracy.
Compared to recent SLAM methods employing neural implicit representations, our
method utilizes a real-time differentiable splatting rendering pipeline that
offers significant speedup to map optimization and RGB-D re-rendering.
Specifically, we propose an adaptive expansion strategy that adds new or
deletes noisy 3D Gaussian in order to efficiently reconstruct new observed
scene geometry and improve the mapping of previously observed areas. This
strategy is essential to extend 3D Gaussian representation to reconstruct the
whole scene rather than synthesize a static object in existing methods.
Moreover, in the pose tracking process, an effective coarse-to-fine technique
is designed to select reliable 3D Gaussian representations to optimize camera
pose, resulting in runtime reduction and robust estimation. Our method achieves
competitive performance compared with existing state-of-the-art real-time
methods on the Replica, TUM-RGBD datasets. The source code will be released
upon acceptance.

本文介绍了一种名为 GS-SLAM 的算法，它在同时定位与地图构建（SLAM）系统中首次使用了 3D 高斯表示方法，实现了效率和准确性之间的更好平衡。与使用神经隐式表示的最新 SLAM 方法相比，我们的方法采用了实时可微分雀斑光照渲染流水线，大大加速了地图优化和 RGB-D 重渲染。具体而言，我们提出了一种自适应扩张策略，通过添加新的或删除噪音 3D 高斯来有效重构新观测到的场景几何并改善先前观测区域的建图。这种策略对于将 3D 高斯表示扩展到重建整个场景而不是在现有方法中合成静态物体至关重要。此外，在位姿跟踪过程中，设计了一种有效的从粗到细的技术，以选择可靠的 3D 高斯表示来优化相机姿态，从而减少运行时间并实现强健的估计。我们的方法在 Replica 和 TUM-RGBD 数据集上与现有的最新实时方法具有竞争力的性能。源代码将在获批后发布。