The field of 3D scene reconstruction is witnessing significant advancements with the development of innovative techniques based on 3D Gaussian Splatting. A key direction in this research area involves improving the efficiency and accuracy of 3D Gaussian Splatting methods, enabling the reconstruction of complex scenes with high fidelity. Researchers are exploring novel approaches to optimize the number of Gaussian primitives required for reconstruction, thereby reducing computational costs and memory usage. Another area of focus is the integration of differentiable rendering and mesh extraction techniques, allowing for the direct reconstruction of surface meshes from Gaussian splats. These advancements have the potential to revolutionize various applications, including physics simulations, animation, and synthetic aperture radar imaging. Noteworthy papers in this area include:

• SAR-GS, which presents a novel approach for 3D target reconstruction from synthetic aperture radar imagery using differentiable Gaussian splatting.
• From Coarse to Fine, which introduces a learnable discrete wavelet transform framework for efficient 3D Gaussian Splatting.
• MILo, which proposes a mesh-in-the-loop Gaussian Splatting framework for detailed and efficient surface reconstruction.