Advancements in 3D Gaussian Splatting

The field of 3D Gaussian Splatting is witnessing significant developments, with a focus on improving rendering speed, reducing memory consumption, and enhancing reconstruction quality. Researchers are exploring innovative methods to accelerate 3D Gaussian Splatting, including tile-grouping-based accelerators and codebook-condensed representations. These advancements have the potential to enable real-time applications and high-quality reconstructions of complex scenes. Noteworthy papers include GS-TG, which achieves an average speed-up of 1.54 times over state-of-the-art 3D-GS accelerators, and ContraGS, which significantly reduces memory consumption during training and rendering. Additionally, the introduction of multi-GPU extensions and quantization schemes is expanding the capabilities of 3D Gaussian Splatting for high-resolution isosurface visualization and large-scale scene reconstruction.

Sources

GS-TG: 3D Gaussian Splatting Accelerator with Tile Grouping for Reducing Redundant Sorting while Preserving Rasterization Efficiency

PVINet: Point-Voxel Interlaced Network for Point Cloud Compression

Efficient Geometry Compression and Communication for 3D Gaussian Splatting Point Clouds

ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction

Toward Distributed 3D Gaussian Splatting for High-Resolution Isosurface Visualization

3DOF+Quantization: 3DGS quantization for large scenes with limited Degrees of Freedom

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