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
MVGS: Multi-view-regulated Gaussian Splatting for Novel View Synthesis
Xiaobiao Du, Yida Wang, Xin Yu
3DGS-DET: Empower 3D Gaussian Splatting with Boundary Guidance and Box-Focused Sampling for 3D Object Detection
Yang Cao, Yuanliang Jv, Dan Xu
GaussianBlock: Building Part-Aware Compositional and Editable 3D Scene by Primitives and Gaussians
Shuyi Jiang, Qihao Zhao, Hossein Rahmani, De Wen Soh, Jun Liu, Na Zhao
UW-GS: Distractor-Aware 3D Gaussian Splatting for Enhanced Underwater Scene Reconstruction
Haoran Wang, Nantheera Anantrasirichai, Fan Zhang, David Bull
EVA-Gaussian: 3D Gaussian-based Real-time Human Novel View Synthesis under Diverse Camera Settings
Yingdong Hu, Zhening Liu, Jiawei Shao, Zehong Lin, Jun Zhang
GaRField++: Reinforced Gaussian Radiance Fields for Large-Scale 3D Scene Reconstruction
Hanyue Zhang, Zhiliu Yang, Xinhe Zuo, Yuxin Tong, Ying Long, Chen Liu
Spectral-GS: Taming 3D Gaussian Splatting with Spectral Entropy
Letian Huang, Jie Guo, Jialin Dan, Ruoyu Fu, Shujie Wang, Yuanqi Li, Yanwen Guo
DrivingForward: Feed-forward 3D Gaussian Splatting for Driving Scene Reconstruction from Flexible Surround-view Input
Qijian Tian, Xin Tan, Yuan Xie, Lizhuang Ma