LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos
Authors: name: Chin-Yang Lin, name: Cheng Sun, name: Fu-En Yang, name: Min-Hung Chen, name: Yen-Yu Lin, name: Yu-Lun Liu
Organization: National Yang Ming Chiao Tung University

LongSplat addresses critical challenges in novel view synthesis (NVS) from
casually captured long videos characterized by irregular camera motion, unknown
camera poses, and expansive scenes. Current methods often suffer from pose
drift, inaccurate geometry initialization, and severe memory limitations. To
address these issues, we introduce LongSplat, a robust unposed 3D Gaussian
Splatting framework featuring: (1) Incremental Joint Optimization that
concurrently optimizes camera poses and 3D Gaussians to avoid local minima and
ensure global consistency; (2) a robust Pose Estimation Module leveraging
learned 3D priors; and (3) an efficient Octree Anchor Formation mechanism that
converts dense point clouds into anchors based on spatial density. Extensive
experiments on challenging benchmarks demonstrate that LongSplat achieves
state-of-the-art results, substantially improving rendering quality, pose
accuracy, and computational efficiency compared to prior approaches. Project
page: https://linjohnss.github.io/longsplat/