InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules
This addresses the problem of scene generalization in NeRF for computer vision and graphics applications, offering a flexible integration pathway, though it appears incremental as it builds on existing NeRF frameworks.
The paper tackles the challenge of generalizing Neural Radiance Fields (NeRF) to new scenes by introducing InsertNeRF, which uses HyperNet modules to dynamically adapt NeRF's weights, resulting in more accurate and efficient representations of complex appearances and geometries.
Generalizing Neural Radiance Fields (NeRF) to new scenes is a significant challenge that existing approaches struggle to address without extensive modifications to vanilla NeRF framework. We introduce InsertNeRF, a method for INStilling gEneRalizabiliTy into NeRF. By utilizing multiple plug-and-play HyperNet modules, InsertNeRF dynamically tailors NeRF's weights to specific reference scenes, transforming multi-scale sampling-aware features into scene-specific representations. This novel design allows for more accurate and efficient representations of complex appearances and geometries. Experiments show that this method not only achieves superior generalization performance but also provides a flexible pathway for integration with other NeRF-like systems, even in sparse input settings. Code will be available https://github.com/bbbbby-99/InsertNeRF.