Improving Neural Radiance Fields with Depth-aware Optimization for Novel View Synthesis

Understand

With dense inputs, Neural Radiance Fields (NeRF) is able to render photo-realistic novel views under static conditions.

• Although the synthesis quality is excellent, existing NeRF-based methods fail to obtain moderate three-dimensional (3D) structures.
• The novel view synthesis quality drops dramatically given sparse input due to the implicitly reconstructed inaccurate 3D-scene structure.
• We propose SfMNeRF, a method to better synthesize novel views as well as reconstruct the 3D-scene geometry.