title: COLMAP-Free 3D Gaussian Splatting
summary: 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
paper_authors._id: 65793cf60766f274f3fae75d
paper_authors.name: Yang Fu
paper_authors._id: 65793cf60766f274f3fae75e
paper_authors.name: Sifei Liu
paper_authors._id: 65793cf60766f274f3fae75f
paper_authors.name: Amey Kulkarni
paper_authors._id: 65793cf60766f274f3fae760
paper_authors.name: Jan Kautz
paper_authors._id: 65793cf60766f274f3fae761
paper_authors.name: Alexei A. Efros
paper_authors._id: 65793cf60766f274f3fae762
paper_authors.name: Xiaolong Wang
paper_title: COLMAP-Free 3D Gaussian Splatting
paper_summary: 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
paper_ai_summary: The paper presents a method for novel view synthesis and camera pose estimation using 3D Gaussian Splatting, eliminating the need for pre-processed camera poses by processing input frames sequentially.
paper_ai_keywords: Neural Radiance Fields (NeRFs) 3D Gaussian Splatting explicit geometric representation input video stream 3D Gaussians set view synthesis camera pose estimation