RGB-Only Supervised Camera Parameter Optimization in Dynamic Scenes
Fang Li, Hao Zhang, Narendra Ahuja
Although COLMAP has long remained the predominant method for camera parameter optimization in static scenes, it is constrained by its lengthy runtime and reliance on ground truth (GT) motion masks for application to dynamic scenes. Many efforts attempted to improve it by incorporating more priors as supervision such as GT focal length, motion masks, 3D point clouds, camera poses, and metric depth, which, however, are typically unavailable in casually captured RGB videos. In this paper, we propose a novel method for more $\textbf{accurate}$ and $\textbf{efficient}$ camera parameter optimization in dynamic scenes $\textbf{solely supervised by a single RGB video}$. Our method consists of three key components: (1) $\textit{Patch-wise Tracking Filters}$, to establish robust and maximally sparse hinge-like relations across the RGB video. (2) $\textit{Outlier-aware Joint Optimization}$, for efficient camera parameter optimization by adaptive down-weighting of moving outliers, without reliance on motion priors. (3) A $\textit{Two-stage Optimization Strategy}$, to enhance stability and optimization speed by a trade-off between the Softplus limits and convex minima in losses. We visually and numerically evaluate our camera estimates. To further validate accuracy, we feed the camera estimates into a 4D reconstruction method and assess the resulting 3D scenes, and rendered 2D RGB and depth maps. We perform experiments on 4 real-world datasets (NeRF-DS, DAVIS, iPhone, and TUM-dynamics) and 1 synthetic dataset (MPI-Sintel), demonstrating that our method estimates camera parameters more efficiently and accurately with a single RGB video as the only supervision. Code will be released upon acceptance.