Hybrid bundle-adjusting 3D Gaussians for view consistent rendering with pose optimization

Yanan Guo; Ying Xie; Ying Chang; Benkui Zhang; Bo Jia; Lin Cao

doi:10.1117/12.3037383

22 November 2024 Hybrid bundle-adjusting 3D Gaussians for view consistent rendering with pose optimization

Yanan Guo, Ying Xie, Ying Chang, Benkui Zhang, Bo Jia, Lin Cao

Author Affiliations +

Proceedings Volume 13239, Optoelectronic Imaging and Multimedia Technology XI; 132391Q (2024) https://doi.org/10.1117/12.3037383
Event: SPIE/COS Photonics Asia, 2024, Nantong, Jiangsu, China

Conference Poster

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanan Guo, Ying Xie, Ying Chang, Benkui Zhang, Bo Jia, and Lin Cao "Hybrid bundle-adjusting 3D Gaussians for view consistent rendering with pose optimization", Proc. SPIE 13239, Optoelectronic Imaging and Multimedia Technology XI, 132391Q (22 November 2024); https://doi.org/10.1117/12.3037383

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