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Paper · arXiv 2508.14041

LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos

Chin-Yang Lin, Cheng Sun, Fu-En Yang, Min-Hung Chen, Yen-Yu Lin, Yu-Lun Liu

59 upvotesAugust 19, 2025arXiv 预印本
AI 摘要

LongSplat improves novel view synthesis from long videos with irregular motion through joint optimization, robust pose estimation, and efficient anchor formation.

novel view synthesis3D Gaussian SplattingIncremental Joint Optimizationcamera poses3D GaussiansPose Estimation Module3D priorsOctree Anchor Formationdense point cloudsanchorsrendering qualitypose accuracycomputational efficiency

Abstract

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/

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