LAENeRF: Local Appearance Editing for Neural Radiance Fields

Основні поняття

Unified framework for photorealistic and non-photorealistic appearance editing of NeRFs.

Анотація

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:

1. Introduction
   • NeRFs revolutionize novel view synthesis.
   • Challenges in local appearance editing.
2. Related Work
   • Photorealistic and non-photorealistic appearance editing methods.
3. Preliminaries
   • Overview of Neural Radiance Fields (NeRFs).
4. LAENeRF
   • Key insight on reducing computational requirements.
   • Network architecture for local appearance editing.
5. Experiments
   • Datasets used for evaluation.
   • Quantitative and qualitative results for recoloring and stylization tasks.
6. User Study
   • Comparison with PaletteNeRF and Ref-NPR through a user study.

Статистика

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.

Цитати

"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."

Ключові висновки, отримані з

LAENeRF

by Lukas Radl,M... о arxiv.org 03-26-2024

https://arxiv.org/pdf/2312.09913.pdf

Глибші Запити

How can LAENeRF's approach be applied to other types of neural networks or models

LAENeRF's approach of palette-based decomposition can be applied to other types of neural networks or models that deal with color editing or stylization tasks. For instance, it could be utilized in image processing applications where interactive recoloring or stylization is required, such as in image editing software or graphic design tools. By incorporating a similar framework of learning a mapping from input positions to output colors via a palette-based formulation, other models can also achieve efficient and intuitive color edits.

What are the potential limitations or drawbacks of using palette-based decomposition in neural radiance fields

One potential limitation of using palette-based decomposition in neural radiance fields is the constraint it imposes on the range and diversity of colors that can be represented. Since the model learns a fixed set of base colors with barycentric weights for each region, there may be limitations in accurately capturing complex textures or intricate color variations present in real-world scenes. Additionally, the effectiveness of the approach heavily relies on the quality and representativeness of the initial palette chosen for training, which could lead to suboptimal results if not carefully curated.

How might the concept of interactive recoloring be extended to other applications beyond neural radiance fields

The concept of interactive recoloring demonstrated by LAENeRF can be extended to various other applications beyond neural radiance fields. One potential application could be in virtual reality (VR) environments where users can dynamically change the appearance and colors of objects within a simulated space. This would enhance user engagement and customization options within VR experiences. Furthermore, interactive recoloring techniques could also find utility in fashion design software, interior design tools, digital art platforms, and even video editing applications where real-time adjustments to visual elements are desired for creative expression and personalization purposes. The versatility and flexibility offered by interactive recoloring make it applicable across diverse domains requiring dynamic visual modifications.