顶会论文种子 NeRF-Edit: 3D-Supervised Global Editing of Neural Radiance Fields

Title: "NeRF-Edit: 3D-Supervised Global Editing of Neural Radiance Fields"

Abstract: Neural Radiance Fields (NeRFs) have emerged as a powerful representation for 3D scenes, but their manipulation for editing purposes remains a challenging problem. We introduce NeRF-Edit, a novel technique for 3D-supervised global editing of NeRFs. Our approach leverages a complete 3D model as a supervisory signal to guide the modification of a target NeRF. By encoding both the target and supervisory 3D models into a shared latent space, we establish dense correspondences that enable accurate and detailed control over the editing process. NeRF-Edit allows for a wide range of global edits, including shape transformations, style transfer, and attribute manipulation, while preserving fine details and ensuring consistency with the 3D supervisory signal. We demonstrate the effectiveness of our method on a variety of 3D scenes and editing tasks, showcasing its ability to produce high-quality, realistic, and diverse outputs.

Keywords: Neural Radiance Fields, 3D Editing, Global Editing, 3D Supervision, Shape Manipulation, Style Transfer

TOC

1. Introduction
   • 1.1 Background on NeRFs and 3D Shape Editing
     • 1.1.1 Neural Radiance Fields
     • 1.1.2 3D Shape Editing Techniques
   • 1.2 Problem Statement and Motivation
   • 1.3 Proposed Approach and Contributions
2. Related Work
   • 2.1 Neural Radiance Fields 
     • 2.1.1 NeRF for Novel View Synthesis
     • 2.1.2 NeRF Editing and Manipulation
   • 2.2 3D Shape Editing Techniques
     • 2.2.1 Mesh-based Editing
     • 2.2.2 Point Cloud-based Editing
     • 2.2.3 Implicit Representation Editing
   • 2.3 3D Supervision in Shape Editing 
     • 2.3.1 3D Supervision for Shape Completion
     • 2.3.2 3D Supervision for Shape Generation
3. Methodology
   • 3.1 NeRF Representation and Encoding (Algorithm 1)
     • 3.1.1 NeRF Architecture
     • 3.1.2 Latent Space Encoding
   • 3.2 3D Supervision Encoding (Algorithm 2)
     • 3.2.1 Point Cloud Encoding
     • 3.2.2 Mesh Encoding
   • 3.3 Global Editing Operations (Algorithm 3)
     • 3.3.1 Shape Transformations
     • 3.3.2 Style Transfer
     • 3.3.3 Attribute Manipulation
   • 3.4 Training and Optimization
     • 3.4.1 Loss Function
     • 3.4.2 Optimization Strategy
4. Experiments
   • 4.1 Experimental Setup
     • 4.1.1 Datasets
     • 4.1.2 Implementation Details
   • 4.2 Datasets and Evaluation Metrics
     • 4.2.1 ShapeNet 
     • 4.2.2 Thingi10K
   • 4.3 Qualitative Results
     • 4.3.1 Shape Transformations (Figure 5)
     • 4.3.2 Style Transfer (Figure 6)
   • 4.4 Quantitative Results (Table 1, Table 2)
     • 4.4.1 Geometric Accuracy
     • 4.4.2 Consistency with Supervision
   • 4.5 Comparison with Baselines (Figure 7, Figure 8, Table 3)
     • 4.5.1 Image-based Editing Methods
     • 4.5.2 3D Shape Editing Methods
5. Discussion
   • 5.1 Analysis of Results
   • 5.2 Limitations and Future Work
6. Conclusion

Figures

• Figure 1: Illustration of different 3D supervision types (dense point cloud, sparse point cloud, mesh).
• Figure 2: Overview of the NeRF-Edit pipeline.
• Figure 3: Visualization of the latent space encoding for NeRF and 3D supervision.
• Figure 4: Examples of global editing operations (shape transformations, style transfer, attribute manipulation).
• Figure 5: Qualitative results on ShapeNet dataset, showing various editing tasks.
• Figure 6: Qualitative results on Thingi10K dataset, showing various editing tasks.
• Figure 7: Comparison with image-based editing methods.
• Figure 8: Comparison with 3D shape editing methods.

Tables

• Table 1: Quantitative results on ShapeNet dataset (geometric accuracy, consistency with supervision).
• Table 2: Quantitative results on Thingi10K dataset (geometric accuracy, consistency with supervision).
• Table 3: Comparison with baselines (geometric accuracy, consistency with supervision).

Algorithms

• Algorithm 1: NeRF Encoding Algorithm (for encoding the target NeRF into a latent representation)
• Algorithm 2: 3D Supervision Encoding Algorithm (for encoding the 3D supervision signal into a latent representation)
• Algorithm 3: Global Editing Operation Algorithm (for applying global edits to the latent representation of the NeRF)
• Algorithm 4: NeRF Decoding Algorithm (for decoding the edited latent representation back into a NeRF)