  Neural Radiance Fields (NeRF) have garnered remarkable success in novel view
synthesis. Nonetheless, the task of generating high-quality images for novel
views persists as a critical challenge. While the existing efforts have
exhibited commendable progress, capturing intricate details, enhancing
textures, and achieving superior Peak Signal-to-Noise Ratio (PSNR) metrics
warrant further focused attention and advancement. In this work, we propose
NeRF-VPT, an innovative method for novel view synthesis to address these
challenges. Our proposed NeRF-VPT employs a cascading view prompt tuning
paradigm, wherein RGB information gained from preceding rendering outcomes
serves as instructive visual prompts for subsequent rendering stages, with the
aspiration that the prior knowledge embedded in the prompts can facilitate the
gradual enhancement of rendered image quality. NeRF-VPT only requires sampling
RGB data from previous stage renderings as priors at each training stage,
without relying on extra guidance or complex techniques. Thus, our NeRF-VPT is
plug-and-play and can be readily integrated into existing methods. By
conducting comparative analyses of our NeRF-VPT against several NeRF-based
approaches on demanding real-scene benchmarks, such as Realistic Synthetic 360,
Real Forward-Facing, Replica dataset, and a user-captured dataset, we
substantiate that our NeRF-VPT significantly elevates baseline performance and
proficiently generates more high-quality novel view images than all the
compared state-of-the-art methods. Furthermore, the cascading learning of
NeRF-VPT introduces adaptability to scenarios with sparse inputs, resulting in
a significant enhancement of accuracy for sparse-view novel view synthesis. The
source code and dataset are available at
\url{https://github.com/Freedomcls/NeRF-VPT}.
