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
DepthSplat: Connecting Gaussian Splatting and Depth
Haofei Xu, Songyou Peng, Fangjinhua Wang, Hermann Blum, Daniel Barath, Andreas Geiger, Marc Pollefeys
DN-4DGS: Denoised Deformable Network with Temporal-Spatial Aggregation for Dynamic Scene Rendering
Jiahao Lu, Jiacheng Deng, Ruijie Zhu, Yanzhe Liang, Wenfei Yang, Tianzhu Zhang, Xu Zhou
L3DG: Latent 3D Gaussian Diffusion
Barbara Roessle, Norman Müller, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder, Angela Dai, Matthias Nießner
Hybrid bundle-adjusting 3D Gaussians for view consistent rendering with pose optimization
Yanan Guo, Ying Xie, Ying Chang, Benkui Zhang, Bo Jia, Lin Cao
UniG: Modelling Unitary 3D Gaussians for View-consistent 3D Reconstruction
Jiamin Wu, Kenkun Liu, Yukai Shi, Xiaoke Jiang, Yuan Yao, Lei Zhang
Poison-splat: Computation Cost Attack on 3D Gaussian Splatting
Jiahao Lu, Yifan Zhang, Qiuhong Shen, Xinchao Wang, Shuicheng Yan
Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics
Junyi Cao, Shanyan Guan, Yanhao Ge, Wei Li, Xiaokang Yang, Chao Ma
Efficient Perspective-Correct 3D Gaussian Splatting Using Hybrid Transparency
Florian Hahlbohm, Fabian Friederichs, Tim Weyrich, Linus Franke, Moritz Kappel, Susana Castillo, Marc Stamminger, Martin Eisemann, Marcus Magnor