In this paper, we explore the potential of Snapshot Com- pressive Imaging (SCI) technique for recovering the under- lying 3D scene representation from a single temporal com- pressed image. SCI is a cost-effective method that enables the recording of high-dimensional data, such as hyperspec- tral or temporal information, into a single image using low- cost 2D imaging sensors. To achieve this, a series of spe- cially designed 2D masks are usually employed, which not only reduces storage requirements but also offers potential privacy protection. Inspired by this, to take one step further, our approach builds upon the powerful 3D scene represen- tation capabilities of neural radiance fields (NeRF). Specif- ically, we formulate the physical imaging process of SCI as part of the training of NeRF, allowing us to exploit its impressive performance in capturing complex scene struc- tures. To assess the effectiveness of our method, we con- duct extensive evaluations using both synthetic data and real data captured by our SCI system. Extensive experi- mental results demonstrate that our proposed approach sur- passes the state-of-the-art methods in terms of image re- construction and novel view image synthesis. Moreover, our method also exhibits the ability to restore high frame- rate multi-view consistent images by leveraging SCI and the rendering capabilities of NeRF. The code is available at https://github.com/WU-CVGL/SCINeRF.