SS-NeRF: Physically Based Sparse Spectral Rendering With Neural Radiance Field.

Area of Science:

• Computer Vision
• Computer Graphics
• Machine Learning

Background:

• Neural Radiance Fields (NeRF) enable realistic rendering but often require dense input views.
• Existing spectral rendering methods struggle with sparse input data, limiting scene reconstruction accuracy.

Purpose of the Study:

• To propose SS-NeRF, an end-to-end architecture for high-quality, physically based rendering using sparse inputs.
• To enhance scene representation detail and improve novel view synthesis performance with limited viewpoints.

Main Methods:

• Developed SpectralMLP to generate spectral radiance fields and SAUNet for RGB image output from spectrum maps.
• Introduced depth-aware continuity, geometric-projected consistency, and superpixel-aligned consistency for optimized reconstruction with sparse data.

Main Results:

• SS-NeRF effectively enhances scene representation detail from sparse inputs.
• The method demonstrates superior performance in novel view synthesis compared to state-of-the-art approaches.
• Experimental results validate effectiveness on both synthetic and real-world datasets.

Conclusions:

• SS-NeRF offers a robust solution for high-quality rendering with sparse input data.
• The proposed techniques significantly improve NeRF-based methods for scene reconstruction and view synthesis.
• SS-NeRF advances physically based rendering by enabling detailed scene decomposition and reconstruction from limited viewpoints.