מושגי ליבה
Unified framework for photorealistic and non-photorealistic appearance editing of NeRFs.
תקציר
The content introduces LAENeRF, a method for local appearance editing of Neural Radiance Fields. It addresses limitations in current approaches by enabling interactive recoloring and stylization of selected regions while minimizing background artifacts. LAENeRF combines palette-based decomposition with perceptual losses to achieve high-fidelity results. The article discusses the challenges in editing implicit 3D representations and highlights the key insights, architecture, and experimental results of LAENeRF.
Directory:
- Introduction
- NeRFs revolutionize novel view synthesis.
- Challenges in local appearance editing.
- Related Work
- Photorealistic and non-photorealistic appearance editing methods.
- Preliminaries
- Overview of Neural Radiance Fields (NeRFs).
- LAENeRF
- Key insight on reducing computational requirements.
- Network architecture for local appearance editing.
- Experiments
- Datasets used for evaluation.
- Quantitative and qualitative results for recoloring and stylization tasks.
- User Study
- Comparison with PaletteNeRF and Ref-NPR through a user study.
סטטיסטיקה
Due to the omnipresence of Neural Radiance Fields (NeRFs), interest towards editable implicit 3D representations has surged over the last years.
LAENeRF is trained for 1 × 10^5 iterations with previews available after ∼20s.
LAENeRF outperforms previous methods, reducing error rates by 59% compared to PaletteNeRF with semantic guidance.
ציטוטים
"We propose LAENeRF, a unified framework for photorealistic and non-photorealistic appearance editing of NeRFs."
"LAENeRF elegantly combines a palette-based decomposition with perceptual losses."