NeuS-PIR: Learning Relightable Neural Surface using Pre-Integrated Rendering

Conceitos Básicos

Recovering geometry, material properties, and illumination from multi-view images using NeuS-PIR method.

Resumo

This paper introduces NeuS-PIR for reconstructing relightable neural surfaces. It utilizes implicit neural surface representation to factorize geometry, material, and illumination. The method distills indirect illumination fields and enables advanced applications like relighting. Experimental results show superiority over existing methods.

1. Introduction

   • Inverse rendering challenges in computer vision.
   • Importance of recovered properties for various applications.

2. Methodology

   • Utilizing NeuS for geometry reconstruction.
   • Joint optimization with pre-integrated rendering for material and illumination.

3. Data Extraction

   • "Our method excels in relighting the image and reconstructing geometry."

4. Related Work

   • Comparison with explicit reconstruction methods.
   • Advancements in neural surface reconstruction.

5. Experiments

   • Evaluation on synthetic datasets (NeRFactor) and real-world CO3D dataset.

6. Ablation Study

   • Impact of material and SDF regularization on performance.

7. Indirect Illumination

   • Distillation of indirect illumination fields for complex lighting effects.

Estatísticas

"Our method excels in relighting the image and reconstructing geometry."

Citações

"Our method excels in relighting the image and reconstructing geometry."

Principais Insights Extraídos De

NeuS-PIR

by Shi Mao,Chen... às arxiv.org 03-26-2024

https://arxiv.org/pdf/2306.07632.pdf

Perguntas Mais Profundas

How can NeuS-PIR be applied to other domains beyond computer vision

NeuS-PIR can be applied to other domains beyond computer vision by leveraging its capabilities in reconstructing relightable neural surfaces. One potential application is in the field of augmented reality (AR) and virtual reality (VR), where realistic lighting effects are crucial for creating immersive experiences. By incorporating NeuS-PIR, AR/VR applications can achieve more accurate and dynamic lighting simulations, enhancing the overall visual quality. Additionally, NeuS-PIR could also be utilized in architectural visualization to create realistic renderings with interactive lighting adjustments, allowing architects and designers to showcase their projects in various lighting conditions.

What are potential limitations or drawbacks of utilizing implicit neural surface representation

One potential limitation of utilizing implicit neural surface representation is the challenge of interpretability. Since implicit models do not have an explicit geometric structure like traditional mesh-based representations, understanding how the model generates surfaces can be complex. This lack of transparency may make it difficult to debug or troubleshoot issues that arise during training or inference. Another drawback is related to memory efficiency; implicit models tend to require more memory compared to explicit representations due to their continuous nature and higher-dimensional parameter space.

How does the distillation of indirect illumination fields impact the overall performance of the method

The distillation of indirect illumination fields plays a significant role in improving the overall performance of NeuS-PIR by enhancing the realism and accuracy of rendered scenes. By distilling indirect illumination fields from learned representations, the method can effectively capture complex lighting effects such as inter-reflections and color bleeding caused by indirect light sources. This additional information contributes to more realistic rendering results with enhanced details and fidelity, making the relighting process more authentic and visually appealing for various applications such as scene reconstruction or material editing.