Towards High-Quality Novel View Synthesis From Nonuniformly Distributed Input Views

Novel view synthesis is a crucial task in the field of visualization and computer graphics. Although current methods, such as neural radiance fields (NeRF), have proven versatile for novel view synthesis, they often assume that input views are uniformly distributed in space. This means that each view captures a portion of the real-world scene, with each area being equally represented in the input views. In contrast, real-world scenarios involve randomly captured images with an uneven spatial distribution of input views, where some views of the scene are observed more frequently than others. This discrepancy poses significant challenges for current methods, underscoring the need to address the issue of nonuniformity in practical applications. To tackle this challenge, we propose a new dataset and an effective approach. Our extensive dataset comprises diverse scenes, capturing images in various real-world environments. Unlike previous novel view synthesis datasets, which have uniform or near-uniform input view distributions, our dataset effectively reflects the nonuniform distribution of images in real-world settings. Additionally, we introduce a simple yet effective method that significantly enhances the performance of current techniques under nonuniform conditions without modifying their pipelines. We anticipate that our data and method will serve as a valuable source of inspiration for future research in this area.