  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.
