Awesome 3D Gaussian Splatting is a curated list of papers and resources focused on the 3D Gaussian Splatting technique and related research.
The project addresses the need to track and organize the growing body of research in 3D Gaussian Splatting, a neural rendering approach that represents 3D scenes using Gaussian primitives. By maintaining a structured collection of papers and resources, the list helps researchers and practitioners stay informed about developments in this rapidly evolving field, which builds on and extends concepts from neural radiance fields.
This resource suits researchers exploring 3D scene representation, practitioners implementing Gaussian Splatting methods, and anyone seeking to understand the landscape of work in neural rendering. The curated nature means entries are selected rather than exhaustive, making it useful for identifying key papers and foundational work rather than serving as a comprehensive archive. Those new to the field can use it as an entry point, while active researchers can monitor emerging contributions.
The project maintains an organized collection that evolves as new research emerges, reflecting ongoing activity in tracking and categorizing papers within the 3D Gaussian Splatting domain.
