Recent studies in Radiance Fields have paved the robust way for novel view synthesis with their photorealistic rendering quality. Nevertheless, they usually employ neural networks and volumetric rendering, which are costly to train and impede their broad use in various real-time applications due to the lengthy rendering time. Lately 3D Gaussians splatting-based approach has been proposed to model the 3D scene, and it achieves remarkable visual quality while rendering the images in real-time. However, it suffers from severe degradation in the rendering quality if the training images are blurry. Blurriness commonly occurs due to the lens defocusing, object motion, and camera shake, and it inevitably intervenes in clean image acquisition. Several previous studies have attempted to render clean and sharp images from blurry input images using neural fields. The majority of those works, however, are designed only for volumetric rendering-based neural radiance fields and are not straightforwardly applicable to rasterization-based 3D Gaussian splatting methods. Thus, we propose a novel real-time deblurring framework, deblurring 3D Gaussian Splatting, using a small Multi-Layer Perceptron (MLP) that manipulates the covariance of each 3D Gaussian to model the scene blurriness. While deblurring 3D Gaussian Splatting can still enjoy real-time rendering, it can reconstruct fine and sharp details from blurry images. A variety of experiments have been conducted on the benchmark, and the results have revealed the effectiveness of our approach for deblurring.
最近在辐射场的研究为新视角合成铺平了一条坚实的道路，其逼真的渲染质量令人印象深刻。然而，它们通常采用神经网络和体积渲染，这在训练上成本高昂，并且由于渲染时间过长，阻碍了它们在各种实时应用中的广泛使用。最近，基于3D高斯涂抹的方法被提出来模拟3D场景，并在实时渲染图像时实现了显著的视觉质量。然而，如果训练图像模糊，它会严重降低渲染质量。由于镜头失焦、物体运动和相机抖动，模糊通常发生，并且不可避免地干扰了清晰图像的获取。一些先前的研究已经尝试使用神经场从模糊输入图像中渲染出清晰锐利的图像。然而，这些工作的大多数只设计用于基于体积渲染的神经辐射场，并不适用于基于光栅化的3D高斯涂抹方法。因此，我们提出了一种新的实时去模糊框架，使用一个小型的多层感知器（MLP）操作每个3D高斯的协方差来模拟场景模糊度，从而去模糊3D高斯涂抹。虽然去模糊3D高斯涂抹仍然可以实现实时渲染，但它可以从模糊图像中重建出细腻和锐利的细节。我们在基准上进行了多种实验，结果显示了我们方法的去模糊效果。