FRPGS: Fast, Robust, and Photorealistic Monocular Dynamic Scene Reconstruction With Deformable 3D Gaussians

Dynamic reconstruction technology presents significant promise for applications in visual and interactive fields. Current techniques utilizing 3D Gaussian Splatting show favorable results and fast reconstruction speed. However, as scene expanding, using individual Gaussian structure (i) leads to instability in large-scale dynamic reconstruction, marked by abrupt deformation, and (ii) the heuristic densification of individuals suffers significant redundancy. Tackling these issues, we propose a jointed Gaussian representation method named FRPGS, which learns the global information and the deformation using center Gaussians and generates the neural Gaussians around them for local detail. Specifically, FRPGS employs center Gaussians initialized from point clouds, which are learned with a deformation field for representing global relationships and dynamic motion over time. Then, for each center Gaussian, attribute networks generate neural Gaussians that move under the linked center Gaussian driving, thereby ensuring structural integrity during movement within this joint-based representation. Finally, to reduce Gaussian redundancy, a densification strategy is developed based on the average cumulative gradient of the associated neural Gaussians, imposing strict limits on the growing of center Gaussians without compromising accuracy. Additionally, we established a large-scale dynamic indoor dataset at the MuLong Laboratory of ZTE Corporation. Evaluations demonstrate that FRPGS significantly outperforms state-of-the-art methods in both training efficiency and reconstruction quality, achieving over a 50% (up to 74%) improvement in efficiency on an RTX 4090. FRPGS also supports the 4K resolution reconstruction of 60 frames simultaneously.