Matrix-Free Shared Intrinsics Bundle Adjustment

Research on accelerating bundle adjustment has focused on photo collections where each image is accompanied by its own set of camera parameters. However, realworld applications overwhelmingly call for shared intrinsics bundle adjustment (SI-BA) where camera parameters are shared across multiple images. Utilizing overlooked optimization opportunities specific to SI-BA, most notably matrix-free computation, we present a solver that is eight times faster than alternatives while consuming a tenth of the memory. Additionally, we examine factors contributing to BA instability under single-precision computation and propose mitigations.