Curated list of papers and resources focused on 3D Gaussian Splatting, intended to keep pace with the anticipated surge of research in the coming months.

[CVPR 2024] Official PyTorch implementation of SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering

3D Gaussian Splat Editor

3D高斯论文，持续更新，欢迎交流讨论。

Full python interactive 3D Gaussian Splatting viewer for real-time editing and analyzing.

A Modular Framework for 3D Gaussian Splatting and Beyond

A comprehensive list of Implicit Representations, NeRF and 3D Gaussian Splatting papers relating to SLAM/Robotics domain, including papers, videos, codes, and related websites

[TVCG2024] PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction

A growing curation of Text-to-3D, Diffusion-to-3D works.

DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshing

A cross-platform, high performance renderer for Gaussian Splatting using Vulkan Compute. Supports ✅ Windows, Linux, macOS, iOS, and visionOS

🧙🏻‍♂️A list of papers curated for you to dive into the Awesome Radiance Field-based 3D Editing.

Official Implementation of Self-Supervised Street Gaussians for Autonomous Driving

File format for 3D Gaussian splats. About 10x smaller than the PLY equivalent with virtually no perceptible loss in visual quality. Offered as open source by Niantic Labs. More details at https://scaniverse.com/spz #3dgaussiansplats #gaussiansplatting

Code for "Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text" (NeurIPS 2024).

Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis

A geometry-shader-based, global CUDA sorted high-performance 3D Gaussian Splatting rasterizer. Can achieve a 5-10x speedup in rendering compared to the vanialla diff-gaussian-rasterization.

[3DV 2025] LoopSplat: Loop Closure by Registering 3D Gaussian Splats

"Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis" (ECCV 2024)

A comprehensive survey of forging vision foundation models for autonomous driving, including challenges, methodologies, and opportunities.