GSsplat is a feed-forward generalizable 3D Gaussian Splatting model that renders novel-view colors and semantic maps from multi-view inputs without per-scene training, claiming state-of-the-art semantic accuracy at the highest speed.
InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules
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abstract
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.
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GSsplat: Generalizable Semantic Gaussian Splatting for Novel-view Synthesis in 3D Scenes
GSsplat is a feed-forward generalizable 3D Gaussian Splatting model that renders novel-view colors and semantic maps from multi-view inputs without per-scene training, claiming state-of-the-art semantic accuracy at the highest speed.