Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur
This addresses the problem of rendering quality in large-scale scenes with artifacts like motion blur for applications in computer vision and graphics, representing an incremental improvement over prior methods.
The paper tackles the challenge of rendering high-fidelity, view-consistent novel views of large-scale scenes from in-the-wild images with motion blur, achieving state-of-the-art results by surpassing existing point-based methods in experiments on real and synthetic data.
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.