Research
Camera Splatting for Continuous View Optimization
arXiv:2509.15677v2 Announce Type: replace Abstract: We propose Camera Splatting, a novel view optimization framework for novel view synthesis. Each camera is modeled as a 3D Gaussian, referred to as a
arXiv:2509.15677v2 Announce Type: replace Abstract: We propose Camera Splatting, a novel view optimization framework for novel view synthesis. Each camera is modeled as a 3D Gaussian, referred to as a camera splat, and virtual cameras, termed point cameras, are placed at 3D points sampled near the surface to observe the distribution of camera splats. View optimization is achieved by continuously and differentiably refining the camera splats so that desirable target distributions are observed from the point cameras, in a manner similar to the original 3D Gaussian splatting. Compared to the Farthest View Sampling (FVS) approach, our optimized views demonstrate superior performance in capturing complex view-dependent phenomena, including intense metallic reflections and intricate textures such as text.
Related
- SA-ResGS: Self-Augmented Residual 3D Gaussian Splatting for Next Best View Selection
- A Step to Decouple Optimization in 3DGS
- D^2-4DGS: Dual-Depth Guided Sparse-Camera 4D Gaussian Splatting
- MVGS: Multi-view Regulated Gaussian Splatting for Novel View Synthesis
Source: arXiv cs.CV | 2026-09-04