Novel view synthesis has made significant progress in the field of 3D computer vision. However, the rendering of view-consistent novel views from imperfect camera poses remains challenging. In this paper, we introduce a hybrid bundle-adjusting 3D Gaussians model that enables view-consistent rendering with pose optimization. This model jointly extract image-based and neural 3D representations to simultaneously generate view-consistent images and camera poses within forward-facing scenes. The effective of our model is demonstrated through extensive experiments conducted on both real and synthetic datasets. These experiments clearly illustrate that our model can effectively optimize neural scene representations while simultaneously resolving significant camera pose misalignments.
新视角合成在3D计算机视觉领域取得了显著进展，然而，从不完美的相机位姿中渲染视角一致的新视图仍然充满挑战。在本文中，我们引入了一种混合捆绑调整的3D高斯模型，该模型通过位姿优化实现视角一致的渲染。该模型联合提取基于图像和神经3D表示，在前向场景中同时生成视角一致的图像和相机位姿。通过在真实和合成数据集上进行的大量实验，我们证明了该模型的有效性。这些实验清晰地展示了该模型能够有效优化神经场景表示，同时解决显著的相机位姿错位问题。