Camera Splatting for Continuous View Optimization

• URL: http://arxiv.org/abs/2509.15677v1
• Date: Fri, 19 Sep 2025 06:50:37 GMT
• Title: Camera Splatting for Continuous View Optimization
• Authors: Gahye Lee, Hyomin Kim, Gwangjin Ju, Jooeun Son, Hyejeong Yoon, Seungyong Lee,
• Abstract summary: Camera Splatting is a novel view optimization framework for novel view synthesis.<n>Point cameras are placed at 3D points sampled near the surface to observe the distribution of camera splats.<n>Compared to the Farthest View Sampling (FVS) approach, our views demonstrate superior performance in capturing complex view-dependent phenomena.
• Score: 7.4969556608280365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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