LAENeRF: Local Appearance Editing for Neural Radiance Fields
(2312.09913)Abstract
Due to the omnipresence of Neural Radiance Fields (NeRFs), the interest towards editable implicit 3D representations has surged over the last years. However, editing implicit or hybrid representations as used for NeRFs is difficult due to the entanglement of appearance and geometry encoded in the model parameters. Despite these challenges, recent research has shown first promising steps towards photorealistic and non-photorealistic appearance edits. The main open issues of related work include limited interactivity, a lack of support for local edits and large memory requirements, rendering them less useful in practice. We address these limitations with LAENeRF, a unified framework for photorealistic and non-photorealistic appearance editing of NeRFs. To tackle local editing, we leverage a voxel grid as starting point for region selection. We learn a mapping from expected ray terminations to final output color, which can optionally be supervised by a style loss, resulting in a framework which can perform photorealistic and non-photorealistic appearance editing of selected regions. Relying on a single point per ray for our mapping, we limit memory requirements and enable fast optimization. To guarantee interactivity, we compose the output color using a set of learned, modifiable base colors, composed with additive layer mixing. Compared to concurrent work, LAENeRF enables recoloring and stylization while keeping processing time low. Furthermore, we demonstrate that our approach surpasses baseline methods both quantitatively and qualitatively.
Overview
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Introduces LAENeRF, a novel method to edit localized appearance in pre-trained NeRFs, supporting both photorealistic and artistic changes.
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Addresses NeRF editing challenges such as limited interactivity, localized change difficulties, and high memory demands.
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Uses a voxel grid for efficient local editing and a learning model to predict an editable color palette for rapid optimization.
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LAENeRF’s architecture enhances user control, with neural modules for predicting color weights, offsets, and novel losses for style.
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Demonstrates superior performance in local editing tasks compared to other methods, providing a tool for practical 3D scene editing.
Introduction to Local Appearance Editing
As digital rendering technologies progress, the manipulation and editing of 3D scenes become increasingly important. One of the primary tools for creating high-fidelity synthetic images is Neural Radiance Fields (NeRFs), which can faithfully reconstruct complex scenes from a set of input images. The work introduces LAENeRF, a novel approach to editing the localized appearance of objects within a pre-trained NeRF. This method allows both photorealistic and non-photorealistic alterations, such as recoloring and stylization, while maintaining the original fidelity of the scene and avoiding unwanted artifacts.
Challenges and Solutions in NeRF Editing
Editing NeRFs presents various challenges, including limited interactivity, lack of support for localized changes, and high memory consumption that result in impractical editing processes. LAENeRF addresses these challenges by enabling efficient local edits using a voxel grid that allows users to select specific content within a 3D scene for editing. It integrates a learning model that predicts an editable color palette based on ray termination points, facilitating rapid and memory-efficient optimizations.
Interactivity and Enhanced Editing Control
The LAENeRF’s architecture includes a neural module that is trained to predict color weights and offsets, which are composed into the output colors using a learned color palette. This setup allows users to alter colors interactively after the optimization process. The method incorporates novel losses, including a style loss, which guides the editing towards photorealistic or stylized outcomes while preserving 3D geometry and ensuring multi-view consistency.
Results and Contributions
The LAENeRF framework is thoroughly tested against concurrent methods and exhibits superior performance for local editing operations. Results show that LAENeRF outperforms other techniques in terms of processing time and quality of results, reducing error rates in recoloring by significant margins. The main contributions of this work include combining photorealistic and non-photorealistic edits for NeRFs, introducing the first interactive method for local and recolorable stylization, and proposing a unique architecture for efficient, 3D-aware stylization. The approach is a step towards practical, artist-friendly editing tools for complex 3D scenes.
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