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

• URL: http://arxiv.org/abs/2508.14041v1
• Date: Tue, 19 Aug 2025 17:59:56 GMT
• Title: LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos
• Authors: Chin-Yang Lin, Cheng Sun, Fu-En Yang, Min-Hung Chen, Yen-Yu Lin, Yu-Lun Liu,
• Abstract summary: LongSplat addresses challenges in novel view synthesis (NVS) from casually captured long videos characterized by irregular camera motion, unknown camera poses, and expansive scenes.<n>LongSplat is a robust unposed 3D Gaussian Splatting framework featuring: (1) Incremental Joint Optimization that concurrently optimize 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.
• Score: 24.61106294159454
• License: http://creativecommons.org/licenses/by/4.0/
• 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/

Related papers

• YoNoSplat: You Only Need One Model for Feedforward 3D Gaussian Splatting [79.38712054342625]
  YoNoSplat is a feedforward model that reconstructs high-quality 3D Gaussian Splatting representations from an arbitrary number of images.<n>Our model is highly versatile, operating effectively with both posed and unposed, calibrated and uncalibrated inputs.<n>It achieves state-of-the-art performance on standard benchmarks in both pose-free and pose-dependent settings.
  arXiv  Detail & Related papers  (2025-11-10T17:21:54Z)
• UPGS: Unified Pose-aware Gaussian Splatting for Dynamic Scene Deblurring [31.35713139629235]
  Reconstructing 3D scenes from monocular video often fails due to severe motion blur caused by camera and object motion.<n>We introduce a unified optimization framework by incorporating camera poses as learnable parameters.<n>Our method achieves significant gains in reconstruction quality and pose estimation accuracy over prior dynamic deblurring methods.
  arXiv  Detail & Related papers  (2025-08-31T13:01:03Z)
• LangSplatV2: High-dimensional 3D Language Gaussian Splatting with 450+ FPS [60.933341835615465]
  LangSplatV2 achieves high-dimensional feature splatting at 476.2 FPS and 3D open-vocabulary text querying at 384.6 FPS for high-resolution images.<n>LangSplatV2 not only achieves better or competitive query accuracy but is also significantly faster.
  arXiv  Detail & Related papers  (2025-07-09T00:19:58Z)
• RobustSplat: Decoupling Densification and Dynamics for Transient-Free 3DGS [79.15416002879239]
  3D Gaussian Splatting has gained significant attention for its real-time, photo-realistic rendering in novel-view synthesis and 3D modeling.<n>Existing methods struggle with accurately modeling scenes affected by transient objects, leading to artifacts in the rendered images.<n>We propose RobustSplat, a robust solution based on two critical designs.
  arXiv  Detail & Related papers  (2025-06-03T11:13:48Z)
• AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views [57.13066710710485]
  AnySplat is a feed forward network for novel view synthesis from uncalibrated image collections.<n>A single forward pass yields a set of 3D Gaussian primitives encoding both scene geometry and appearance.<n>In extensive zero shot evaluations, AnySplat matches the quality of pose aware baselines in both sparse and dense view scenarios.
  arXiv  Detail & Related papers  (2025-05-29T17:49:56Z)
• Towards Better Robustness: Pose-Free 3D Gaussian Splatting for Arbitrarily Long Videos [24.959777640700178]
  3D Gaussian Splatting (3DGS) has emerged as a powerful representation due to its efficiency and high-fidelity rendering.<n>We propose Rob-GS, a robust framework to progressively estimate camera poses and optimize 3DGS for arbitrarily long video inputs.
  arXiv  Detail & Related papers  (2025-01-25T06:02:20Z)
• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices.<n>Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• Look Gauss, No Pose: Novel View Synthesis using Gaussian Splatting without Accurate Pose Initialization [11.418632671254564]
  3D Gaussian Splatting has emerged as a powerful tool for fast and accurate novel-view synthesis from a set of posed input images. We propose an extension to the 3D Gaussian Splatting framework by optimizing the extrinsic camera parameters with respect to photometric residuals. We show results on real-world scenes and complex trajectories through simulated environments.
  arXiv  Detail & Related papers  (2024-10-11T12:01:15Z)
• SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting [44.42317312908314]
  3D Gaussian Splatting (3DGS) is a promising technique for 3D reconstruction, offering efficient training and rendering speeds. Current methods require highly controlled environments to meet the inter-view consistency assumption of 3DGS. We present SpotLessSplats, an approach that leverages pre-trained and general-purpose features coupled with robust optimization to effectively ignore transient distractors.
  arXiv  Detail & Related papers  (2024-06-28T17:07:11Z)
• A Construct-Optimize Approach to Sparse View Synthesis without Camera Pose [44.13819148680788]
  We develop a novel construct-and-optimize method for sparse view synthesis without camera poses. Specifically, we construct a solution by using monocular depth and projecting pixels back into the 3D world. We demonstrate results on the Tanks and Temples and Static Hikes datasets with as few as three widely-spaced views.
  arXiv  Detail & Related papers  (2024-05-06T17:36:44Z)
• InstantSplat: Sparse-view Gaussian Splatting in Seconds [91.77050739918037]
  We introduce InstantSplat, a novel approach for addressing sparse-view 3D scene reconstruction at lightning-fast speed.<n>InstantSplat employs a self-supervised framework that optimize 3D scene representation and camera poses.<n>It achieves an acceleration of over 30x in reconstruction and improves visual quality (SSIM) from 0.3755 to 0.7624 compared to traditional SfM with 3D-GS.
  arXiv  Detail & Related papers  (2024-03-29T17:29:58Z)
• VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction [59.40711222096875]
  We present VastGaussian, the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets.
  arXiv  Detail & Related papers  (2024-02-27T11:40:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.