Volume Feature Rendering for Fast Neural Radiance Field Reconstruction

• URL: http://arxiv.org/abs/2305.17916v2
• Date: Wed, 31 May 2023 07:03:11 GMT
• Title: Volume Feature Rendering for Fast Neural Radiance Field Reconstruction
• Authors: Kang Han, Wei Xiang and Lu Yu
• Abstract summary: Neural radiance fields (NeRFs) are able to synthesize realistic novel views from multi-view images captured from distinct positions and perspectives. In NeRF's rendering pipeline, neural networks are used to represent a scene independently or transform queried learnable feature vector of a point to the expected color or density. We propose to render the queried feature vectors of a ray first and then transform the rendered feature vector to the final pixel color by a neural network.
• Score: 11.05302598034426
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural radiance fields (NeRFs) are able to synthesize realistic novel views from multi-view images captured from distinct positions and perspectives. In NeRF's rendering pipeline, neural networks are used to represent a scene independently or transform queried learnable feature vector of a point to the expected color or density. With the aid of geometry guides either in occupancy grids or proposal networks, the number of neural network evaluations can be reduced from hundreds to dozens in the standard volume rendering framework. Instead of rendering yielded color after neural network evaluation, we propose to render the queried feature vectors of a ray first and then transform the rendered feature vector to the final pixel color by a neural network. This fundamental change to the standard volume rendering framework requires only one single neural network evaluation to render a pixel, which substantially lowers the high computational complexity of the rendering framework attributed to a large number of neural network evaluations. Consequently, we can use a comparably larger neural network to achieve a better rendering quality while maintaining the same training and rendering time costs. Our model achieves the state-of-the-art rendering quality on both synthetic and real-world datasets while requiring a training time of several minutes.

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