Neural Radiance Field Image Refinement through End-to-End Sampling Point Optimization
arXiv:2410.14958v1h-index: 1IEEJ TRANS ELECTR ELECTRON ENG
Originality Synthesis-oriented
AI Analysis
This addresses rendering quality issues for 3D scene reconstruction and novel view synthesis, but appears incremental.
The paper tackles the problem of artifacts in Neural Radiance Field (NeRF) rendering by optimizing sampling points, resulting in more detailed images.
Neural Radiance Field (NeRF), capable of synthesizing high-quality novel viewpoint images, suffers from issues like artifact occurrence due to its fixed sampling points during rendering. This study proposes a method that optimizes sampling points to reduce artifacts and produce more detailed images.