3D Gaussian
3D Gaussian splatting is an explicit 3D scene representation technique that uses collections of 3D Gaussian functions to model objects and scenes, aiming for high-quality, real-time rendering. Current research focuses on improving the accuracy and efficiency of 3D Gaussian splatting, particularly addressing challenges like view-dependent effects, sparse viewpoints, and the handling of dynamic objects and large-scale scenes through various optimization strategies and model architectures. This approach offers significant advantages in applications such as novel view synthesis, 3D object detection, augmented reality, and even multimodal place recognition by providing a compact yet detailed representation of 3D environments.
Papers
Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting
Joey Wilson, Marcelino Almeida, Min Sun, Sachit Mahajan, Maani Ghaffari, Parker Ewen, Omid Ghasemalizadeh, Cheng-Hao Kuo, Arnie Sen
GVKF: Gaussian Voxel Kernel Functions for Highly Efficient Surface Reconstruction in Open Scenes
Gaochao Song, Chong Cheng, Hao Wang
No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images
Botao Ye, Sifei Liu, Haofei Xu, Xueting Li, Marc Pollefeys, Ming-Hsuan Yang, Songyou Peng
GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting
Xiufeng Huang, Ruiqi Li, Yiu-ming Cheung, Ka Chun Cheung, Simon See, Renjie Wan
PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting
Sunghwan Hong, Jaewoo Jung, Heeseong Shin, Jisang Han, Jiaolong Yang, Chong Luo, Seungryong Kim
FreeGaussian: Guidance-free Controllable 3D Gaussian Splats with Flow Derivatives
Qizhi Chen, Delin Qu, Yiwen Tang, Haoming Song, Yiting Zhang, Dong Wang, Bin Zhao, Xuelong Li