NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections (2008.02268v3)

Abstract: We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs. We build on Neural Radiance Fields (NeRF), which uses the weights of a multilayer perceptron to model the density and color of a scene as a function of 3D coordinates. While NeRF works well on images of static subjects captured under controlled settings, it is incapable of modeling many ubiquitous, real-world phenomena in uncontrolled images, such as variable illumination or transient occluders. We introduce a series of extensions to NeRF to address these issues, thereby enabling accurate reconstructions from unstructured image collections taken from the internet. We apply our system, dubbed NeRF-W, to internet photo collections of famous landmarks, and demonstrate temporally consistent novel view renderings that are significantly closer to photorealism than the prior state of the art.

• The paper introduces a novel framework that leverages latent appearance modeling to synthesize novel views from unstructured photo collections.
• It employs a dual-headed network to separate static geometry from transient elements, achieving a notable average PSNR improvement of 4.4dB over previous models.
• The approach demonstrates robust neural rendering in uncontrolled settings, expanding practical applications in AR/VR, cultural heritage digitization, and 3D reconstruction.

NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections

"NeRF in the Wild" explores extending Neural Radiance Fields (NeRF) to unstructured photo collections captured in real-world settings. Traditional NeRF models are limited to static scenes with consistent lighting, restricting their applicability to controlled conditions. This work addresses challenges such as variable lighting, transient occlusions, and photometric inconsistencies inherent in images sourced from the internet.

Introduction

The authors introduce a methodology to synthesize novel views from unstructured collections of photographs, leveraging the strengths of learning-based neural rendering techniques. The primary challenge addressed is extending NeRF to dynamic and uncontrolled environments where traditional assumptions of static geometry and consistent lighting are violated. These situations commonly occur in large-scale internet photo collections of famous landmarks.

Methodology

The paper introduces several enhancements to the NeRF framework to overcome these challenges:

1. Latent Appearance Modeling: Inspired by Generative Latent Optimization (GLO), this model introduces a per-image latent embedding vector. This embedding captures image-specific photometric variations, such as different exposures, lighting, and post-processing effects. As a result, the model decouples geometry from appearance variations, enabling consistent 3D reconstructions independent of these variations.
2. Transient Objects Handling: The authors propose a dual-headed model to account for transient elements. One head handles static scene components, while the other captures transient, image-dependent elements. This design captures variations without affecting the static geometry. Additionally, an uncertainty field is introduced, modeled as an isotropic normal distribution, to identify and discount noisy regions likely to contain transient elements.

Results

Quantitative and Qualitative Evaluation:

• The model is evaluated on several datasets from the Phototourism dataset, including famous landmarks like Brandenburg Gate, Sacre Coeur, and Trevi Fountain.
• Quantitative metrics indicate significant improvements over existing methods. For instance, NeRF-W achieves an average improvement of 4.4dB in PSNR over NRW in test cases.
• Qualitatively, NeRF-W renders high-fidelity images with temporal consistency, unlike NRW, which exhibits temporal instability and artifacts.

Controllable Appearance:

• By learning a latent embedding space for appearance, the model enables smooth and natural interpolation of lighting and appearance in synthesized views without altering the underlying geometry. This feature is demonstrated through interpolations between different appearance embeddings, showcasing the model's ability to reconcile disparate lighting conditions into a coherent 3D space.

Robustness to Uncontrolled Settings:

• NeRF-W's robustness to photometric inconsistencies and transient occlusions is highlighted through experiments. Compared to NeRF, NeRF-W produces geometrically consistent scenes even under significant lighting variations and occlusions.

Implications and Future Work

The implications of this work are substantial for applications in augmented and virtual reality (AR/VR), cultural heritage digitization, and 3D reconstruction from crowd-sourced data. The ability to generate consistent, high-fidelity 3D models from unstructured photos broadens the potential for immersive applications and digital preservation efforts.

Future research might explore enhancing performance under extreme conditions, further reduce computational requirements, or extend capabilities to account for dynamic scenes. The integration of broader contextual understanding, such as semantic consistency, alongside geometric consistency, presents another avenue for exploration.

Conclusion

NeRF in the Wild represents a significant step forward in the field of neural rendering, addressing the longstanding limitations of rendering from unstructured photo collections. By accommodating varying illumination and transient occlusions, this approach extends the applicability of NeRF to real-world, unconstrained environments, achieving state-of-the-art photorealism and temporal consistency in novel view synthesis. The model stands as a pivotal advancement in utilizing neural techniques for practical, large-scale 3D scene reconstruction.