CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural Rendering

Abstract: Generalizability and few-shot learning are key challenges in Neural Radiance Fields (NeRF), often due to the lack of a holistic understanding in pixel-level rendering. We introduce CaesarNeRF, an end-to-end approach that leverages scene-level CAlibratEd SemAntic Representation along with pixel-level representations to advance few-shot, generalizable neural rendering, facilitating a holistic understanding without compromising high-quality details. CaesarNeRF explicitly models pose differences of reference views to combine scene-level semantic representations, providing a calibrated holistic understanding. This calibration process aligns various viewpoints with precise location and is further enhanced by sequential refinement to capture varying details. Extensive experiments on public datasets, including LLFF, Shiny, mip-NeRF 360, and MVImgNet, show that CaesarNeRF delivers state-of-the-art performance across varying numbers of reference views, proving effective even with a single reference image.

• The paper introduces CaesarNeRF, which fuses calibrated semantic and pixel-level features to enhance few-shot neural rendering.
• The method calibrates semantic representations by aligning view-dependent features, significantly reducing bias with minimal reference images.
• Extensive experiments show that CaesarNeRF sets new benchmarks, achieving photorealistic views even with a single reference image.

Introduction

Neural Radiance Fields (NeRF) have emerged as a powerful technology for generating photorealistic images from novel viewpoints. However, applications often face two significant challenges: generalizability across different scenes and the need for only a few reference images to generate these views, a task known as few-shot learning. Many traditional NeRF methods struggle with these issues, as they either require extensive retraining for each new scene or a large number of reference images, which is not always feasible.

Semantic Scene Representation

To address these limitations, a new approach named CaesarNeRF has been developed, which significantly enhances generalizability and performance in few-shot scenarios. CaesarNeRF utilizes an end-to-end pipeline that integrates calibrated semantic representations. These high-level semantic features are fused with pixel-level details to comprehend a scene holistically, which improves the consistency of rendered images across different views. This integration is achieved through a shared encoder framework that processes and combines input images' per-pixel features with global semantic vectors representing the entire scene.

Calibration and Sequential Refinement

One of the innovations introduced in CaesarNeRF is the calibration of semantic representations. The method accounts for view-dependent biases that typically arise from using a limited number of reference images. By modeling camera pose transformations and aligning semantic features across various reference views, CaesarNeRF effectively reduces these biases. Furthermore, a sequential refinement process progressively enriches semantic representations throughout the network, capturing intricate details essential for realistic rendering.

Experimental Validation

Extensive experiments on several public datasets demonstrate that CaesarNeRF establishes new state-of-the-art benchmarks, particularly in settings with very few reference images. Remarkably, it excels in generating accurate views with as little as one reference image. Additionally, CaesarNeRF shows versatility as its framework improves performance when integrated into other established NeRF methods, highlighting the adaptability and effectiveness of the approach in diverse rendering contexts.

Conclusion

CaesarNeRF represents a significant advancement in the field of few-shot, generalizable neural rendering. Its capacity to render detailed and coherent visual content from a singular or minimal set of images holds promise for a variety of applications, including photorealistic virtual content creation and augmented reality, where reference imagery might be limited. The technology bridges a critical gap in current NeRF methodologies by introducing an adaptable and robust solution to scene understanding and view synthesis challenges.