  Rendering novel view images is highly desirable for many applications.
Despite recent progress, it remains challenging to render high-fidelity and
view-consistent novel views of large-scale scenes from in-the-wild images with
inevitable artifacts (e.g., motion blur). To this end, we develop a hybrid
neural rendering model that makes image-based representation and neural 3D
representation join forces to render high-quality, view-consistent images.
Besides, images captured in the wild inevitably contain artifacts, such as
motion blur, which deteriorates the quality of rendered images. Accordingly, we
propose strategies to simulate blur effects on the rendered images to mitigate
the negative influence of blurriness images and reduce their importance during
training based on precomputed quality-aware weights. Extensive experiments on
real and synthetic data demonstrate our model surpasses state-of-the-art
point-based methods for novel view synthesis. The code is available at
https://daipengwa.github.io/Hybrid-Rendering-ProjectPage.
