COLMAP-Free 3D Gaussian Splatting
Yang Fu Sifei Liu Amey Kulkarni Jan Kautz Alexei A. Efros Xiaolong Wang
While neural rendering has led to impressive advances in scene reconstruction
and novel view synthesis, it relies heavily on accurately pre-computed camera
poses. To relax this constraint, multiple efforts have been made to train
Neural Radiance Fields (NeRFs) without pre-processed camera poses. However, the
implicit representations of NeRFs provide extra challenges to optimize the 3D
structure and camera poses at the same time. On the other hand, the recently
proposed 3D Gaussian Splatting provides new opportunities given its explicit
point cloud representations. This paper leverages both the explicit geometric
representation and the continuity of the input video stream to perform novel
view synthesis without any SfM preprocessing. We process the input frames in a
sequential manner and progressively grow the 3D Gaussians set by taking one
input frame at a time, without the need to pre-compute the camera poses. Our
method significantly improves over previous approaches in view synthesis and
camera pose estimation under large motion changes. Our project page is
https://oasisyang.github.io/colmap-free-3dgs