Enhancing Close-up Novel View Synthesis via Pseudo-labeling
This addresses a specific challenge in computer vision for applications requiring high-quality close-up renderings, representing an incremental advancement.
The paper tackles the problem of generating detailed close-up views in novel view synthesis, where existing methods like NeRF and 3DGS struggle due to lack of training data, by introducing a pseudo-label-based learning strategy and a new dataset, achieving improved performance as demonstrated in experiments.
Recent methods, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have demonstrated remarkable capabilities in novel view synthesis. However, despite their success in producing high-quality images for viewpoints similar to those seen during training, they struggle when generating detailed images from viewpoints that significantly deviate from the training set, particularly in close-up views. The primary challenge stems from the lack of specific training data for close-up views, leading to the inability of current methods to render these views accurately. To address this issue, we introduce a novel pseudo-label-based learning strategy. This approach leverages pseudo-labels derived from existing training data to provide targeted supervision across a wide range of close-up viewpoints. Recognizing the absence of benchmarks for this specific challenge, we also present a new dataset designed to assess the effectiveness of both current and future methods in this area. Our extensive experiments demonstrate the efficacy of our approach.