Feature radiance fields (FeRF): A multi-level feature fusion method with deep neural network for image synthesis

Abstract

Neural Radiance Field (NeRF) has brought revolutionary changes to the field of image synthesis with its unique ability to generate highly realistic multi-view consistent images from a neural scene representation. However, current NeRF-based methods still largely depend on multiple, precisely posed images, especially for complex or dynamic scenes, limiting their versatility. Furthermore, some recent strategies attempt to integrate simple feature extraction networks with volume rendering techniques to reduce multi-view dependence but create blurry outputs, highlighting the need for more sophisticated feature handling to unlock NeRF's full potential. In this paper, we propose an image synthesis method named FeRF, distinguished by its capacity to perform comprehensive feature extraction on individual unposed images and facilitate feature fusion at any stage. Additionally, we present an "elaborated-feature generation network" (EGN) composed of four modules, which is configured with two advanced feature extraction modules aimed at precisely refining and processing subtle, complex visual features from a single image. Given that the core objective of FeRF is the precise capture and processing of intricate features from the input images, we innovatively incorporated precisely designed attention mechanisms into the network architecture to optimize and highlight the importance of key feature attributes, thereby effectively enhancing their contribution to subsequent volume rendering processes. Extensive experimentation validates the outstanding qualitative and quantitative performance of our proposed network structure. In comparison to current image feature-based generalized image synthesis methods, it achieves superior reconstruction quality and level of detail.