Methods and strategies for improving the novel view synthesis quality of neural radiation field
It addresses the problem of improving image quality in NeRF for researchers and practitioners, but it is incremental as it focuses on reviewing existing methods rather than introducing new ones.
The paper reviews recent methods to enhance the rendering quality of Neural Radiation Fields (NeRF) for novel view synthesis, analyzing technical principles and discussing future directions to aid researchers in understanding the field's evolution.
Neural Radiation Field (NeRF) technology can learn a 3D implicit model of a scene from 2D images and synthesize realistic novel view images. This technology has received widespread attention from the industry and has good application prospects. In response to the problem that the rendering quality of NeRF images needs to be improved, many researchers have proposed various methods to improve the rendering quality in the past three years. The latest relevant papers are classified and reviewed, the technical principles behind quality improvement are analyzed, and the future evolution direction of quality improvement methods is discussed. This study can help researchers quickly understand the current state and evolutionary context of technology in this field, which is helpful in inspiring the development of more efficient algorithms and promoting the application of NeRF technology in related fields.