COLMAP-Free 3D Gaussian Splatting
The COLMAP-Free 3D Gaussian Splatting (CF-3DGS) framework efficiently synthesizes photo-realistic views without needing Structure-from-Motion (SfM) pre-processing, significantly reducing training time and enabling real-time rendering.
Traditional Neural Radiance Fields (NeRFs) methods depend heavily on accurately pre-computed camera poses, a time-consuming process prone to failure in textureless or repetitive regions, limiting their practicality in dynamic scene reconstruction.
CF-3DGS leverages video stream continuity and an explicit point cloud representation. It incrementally constructs 3D Gaussians for each frame, employing affine transformations for direct camera pose optimization. This process eliminates the necessity for pre-set camera poses, simplifying the optimization under large camera movements.
Compared to previous methods, CF-3DGS shows marked improvement in view synthesis and camera pose estimation, especially under large motion changes. It achieves comparable performance to 3DGS models using COLMAP-assisted poses, validating its efficiency in handling dynamic scenes and large camera movements. For instance, in the Tanks and Temples dataset, it achieves a mean Relative Pose Error (RPE) of 0.041 (translation) and 0.069 (rotation), and an Absolute Trajectory Error (ATE) of 0.004, which are significantly lower than baseline methods.
