UHDNeRF: Ultra-High-Definition Neural Radiance Fields

We propose UHDNeRF, a new framework for novel view synthesis on the challenging ultra-high-resolution (e.g., 4K) real-world scenes. Previous NeRF methods are not specifically designed for rendering on extremely high resolutions, leading to burry results with notable detail-losing problems even though trained on 4K images. This is mainly due to the mismatch between the high-resolution inputs and the low-dimensional volumetric representation. To address this issue, we introduce an adaptive implicit-explicit scene representation with which an explicit sparse point cloud is used to boost the performance of an implicit volume on modeling subtle details. Specifically, we reconstruct the complex real-world scene with a frequency separation strategy that the implicit volume learns to represent the low-frequency properties of the whole scene, and the sparse point cloud is used for reproducing high-frequency details. To better explore the information embedded in the point cloud, we extract a global structure feature and a local point-wise feature from the point cloud for each sample located in the high-frequency regions. Furthermore, a patch-based sampling strategy is introduced to reduce the computational cost. The high-fidelity rendering results demonstrate the superiority of our method for retaining high-frequency details at 4K ultra-high-resolution scenarios against state-of-the-art NeRF-based solutions.