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.
To address the issue of reconstruction distortions caused by using non-unified RPC models in 3D reconstruction with multi-view satellite imagery. In this paper, we propose a method termed Rotation Correction Based Neural Radiance Fields for Multi-view Satellite Images. Our method optimizes the satellite camera model and neural radiance field by integrating multi-axial rotational correction into the reconstruction task. By separately correcting the attitude angles along the X, Y, and Z axes, the geometric distortions in multi-view satellite images can be corrected. Furthermore, by setting the ray projection in the neural radiance field under the effect of variable correction rotation variables, our method allows simultaneous optimization of the correction rotation variables and the neural radiance field during back propagation. We conducted experiments using WorldView-3 images from different locations, comparing the reconstruction results of images corrected with traditional BA and our method. Our method achieved the best results.
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