用伪标签提升近距离新视角生成质量，解决传统方法细节丢失问题。
Enhancing Close-up Novel View Synthesis via Pseudo-labeling
- 通过已有数据生成伪标签，为远距离视角提供监督信号
- 在真实近景视角下实现更清晰的细节还原，显著提升图像质量
- 适合关注高精度三维重建与视觉细节生成的研究者
近期方法如神经辐射场（NeRF）和3D高斯泼溅（3DGS）在新视角生成上表现出色，但在训练视角之外的远距离或近景视角下，尤其在近距离视图中，仍难以生成精细图像。核心问题是缺乏针对近景视角的训练数据，导致模型无法准确渲染此类视图。为此，本文提出一种基于伪标签的学习策略，利用现有训练数据生成伪标签，对大量近景视角提供针对性监督。鉴于该挑战尚无基准评测，我们还构建了一个新数据集，用于评估当前及未来方法在此任务上的表现。大量实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
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.
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