Hybrid bundle-adjusting 3D Gaussians for view consistent rendering with pose optimization
This addresses a challenge in 3D computer vision for applications like virtual reality or robotics, but it appears incremental as it builds on existing 3D Gaussian and bundle adjustment methods.
The paper tackles the problem of view-consistent novel view synthesis from imperfect camera poses by introducing a hybrid bundle-adjusting 3D Gaussians model, which jointly optimizes neural scene representations and resolves camera pose misalignments, as demonstrated on real and synthetic datasets.
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. The source code is available at https://github.com/Bistu3DV/hybridBA.