通过联合优化相机位姿与3D高斯，实现前向场景下视角一致的高质量新视图合成。
Hybrid bundle-adjusting 3D Gaussians for view consistent rendering with pose optimization
- 融合图像特征与神经3D表示，同步优化画面与相机位姿。
- 在真实与合成数据上均显著修正了严重错位的相机位姿。
- 适合需要高精度位姿校准的3D视觉重建任务。
新视图合成在三维计算机视觉领域取得了显著进展，但基于不完美相机位姿生成视角一致的新视图仍具挑战。本文提出一种混合捆绑调整的3D高斯模型，可实现带位姿优化的视角一致渲染。该模型联合提取基于图像和神经3D的表征，在前向场景中同时生成视角一致的图像与相机位姿。大量实验在真实与合成数据集上验证了该方法的有效性，结果表明其能有效优化神经场景表示，并同时解决严重的相机位姿错位问题。源代码已开源：https://github.com/Bistu3DV/hybridBA。
原文摘要 · Abstract (English)
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.
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