With their high-fidelity scene representation capability, the attention of
SLAM field is deeply attracted by the Neural Radiation Field (NeRF) and 3D
Gaussian Splatting (3DGS). Recently, there has been a Surge in NeRF-based SLAM,
while 3DGS-based SLAM is sparse. A novel 3DGS-based SLAM approach with a fusion
of deep visual feature, dual keyframe selection and 3DGS is presented in this
paper. Compared with the existing methods, the proposed selectively tracking is
achieved by feature extraction and motion filter on each frame. The joint
optimization of pose and 3D Gaussian runs through the entire mapping process.
Additionally, the coarse-to-fine pose estimation and compact Gaussian scene
representation are implemented by dual keyfeature selection and novel loss
functions. Experimental results demonstrate that the proposed algorithm not
only outperforms the existing methods in tracking and mapping, but also has
less memory usage.

通过深度视觉特征、双关键帧选择和 3D 高斯喷洒，本文提出了一种基于三维高斯喷洒的 SLAM 方法。该方法通过特征提取和运动滤波在每一帧上实现了选择性跟踪，通过整个映射过程进行姿态和三维高斯的联合优化。此外，通过双关键特征选择和新颖的损失函数实现了粗糙到精细的姿态估计和紧凑的高斯场景表示。实验结果表明，该算法不仅在跟踪和建图方面优于现有方法，而且内存使用更少。

MotionGS：通过运动滤波的紧凑高斯点云融合 SLAM

MotionGS : Compact Gaussian Splatting SLAM by Motion Filter

Neural Radiation Field (NeRF) technology can learn a 3D implicit model of a
scene from 2D images and synthesize realistic novel view images. This
technology has received widespread attention from the industry and has good
application prospects. In response to the problem that the rendering quality of
NeRF images needs to be improved, many researchers have proposed various
methods to improve the rendering quality in the past three years. The latest
relevant papers are classified and reviewed, the technical principles behind
quality improvement are analyzed, and the future evolution direction of quality
improvement methods is discussed. This study can help researchers quickly
understand the current state and evolutionary context of technology in this
field, which is helpful in inspiring the development of more efficient
algorithms and promoting the application of NeRF technology in related fields.

NeRF 技术可以从 2D 图像中学习场景的 3D 隐式模型，并合成逼真的新视角图像。本研究分析了过去三年中涉及 NeRF 图像渲染质量改进的最新相关论文，探讨了质量改进方法背后的技术原理，并讨论了质量改进方法未来的演进方向，有助于研究人员快速了解该领域技术的当前状态和演进背景，对于激发更高效算法的开发和推动 NeRF 技术在相关领域的应用具有帮助。

提高深度辐射场小说视图合成质量的方法与策略

Methods and strategies for improving the novel view synthesis quality of  neural radiation field

Although the use of multiple stacks can handle slice-to-volume motion
correction and artifact removal problems, there are still several problems: 1)
The slice-to-volume method usually uses slices as input, which cannot solve the
problem of uniform intensity distribution and complementarity in regions of
different fetal MRI stacks; 2) The integrity of 3D space is not considered,
which adversely affects the discrimination and generation of globally
consistent information in fetal MRI; 3) Fetal MRI with severe motion artifacts
in the real-world cannot achieve high-quality super-resolution reconstruction.
To address these issues, we propose a novel fetal brain MRI high-quality volume
reconstruction method, called the Radiation Diffusion Generation Model (RDGM).
It is a self-supervised generation method, which incorporates the idea of
Neural Radiation Field (NeRF) based on the coordinate generation and diffusion
model based on super-resolution generation. To solve regional intensity
heterogeneity in different directions, we use a pre-trained transformer model
for slice registration, and then, a new regionally Consistent Implicit Neural
Representation (CINR) network sub-module is proposed. CINR can generate the
initial volume by combining a coordinate association map of two different
coordinate mapping spaces. To enhance volume global consistency and
discrimination, we introduce the Volume Diffusion Super-resolution Generation
(VDSG) mechanism. The global intensity discriminant generation from
volume-to-volume is carried out using the idea of diffusion generation, and
CINR becomes the deviation intensity generation network of the volume-to-volume
diffusion model. Finally, the experimental results on real-world fetal brain
MRI stacks demonstrate the state-of-the-art performance of our method.

提出了一种基于 Radiation Diffusion Generation Model (RDGM) 的新型胎儿脑 MRI 高质量体重建方法，该方法利用 Neural Radiation Field (NeRF) 和超分辨率生成的扩散模型，使用预训练的 transformer 模型进行切片配准，进一步增强了体积的全局一致性和区分能力，实验结果表明该方法在真实世界胎儿脑 MRI 堆栈上表现出最先进的性能。