MOSAIC-GS: Monocular Scene Reconstruction via Advanced Initialization for Complex Dynamic Environments

• URL: http://arxiv.org/abs/2601.05368v1
• Date: Thu, 08 Jan 2026 20:48:24 GMT
• Title: MOSAIC-GS: Monocular Scene Reconstruction via Advanced Initialization for Complex Dynamic Environments
• Authors: Svitlana Morkva, Maximum Wilder-Smith, Michael Oechsle, Alessio Tonioni, Marco Hutter, Vaishakh Patil,
• Abstract summary: MOSAIC-GS is a novel, fully explicit, and computationally efficient approach for high-fidelity dynamic scene reconstruction from monocular videos.<n>We leverage multiple geometric cues, such as depth, optical flow, dynamic object segmentation, and point tracking.<n>We demonstrate that MOSAIC-GS achieves substantially faster optimization and rendering compared to existing methods.
• Score: 12.796165448365949
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present MOSAIC-GS, a novel, fully explicit, and computationally efficient approach for high-fidelity dynamic scene reconstruction from monocular videos using Gaussian Splatting. Monocular reconstruction is inherently ill-posed due to the lack of sufficient multiview constraints, making accurate recovery of object geometry and temporal coherence particularly challenging. To address this, we leverage multiple geometric cues, such as depth, optical flow, dynamic object segmentation, and point tracking. Combined with rigidity-based motion constraints, these cues allow us to estimate preliminary 3D scene dynamics during an initialization stage. Recovering scene dynamics prior to the photometric optimization reduces reliance on motion inference from visual appearance alone, which is often ambiguous in monocular settings. To enable compact representations, fast training, and real-time rendering while supporting non-rigid deformations, the scene is decomposed into static and dynamic components. Each Gaussian in the dynamic part of the scene is assigned a trajectory represented as time-dependent Poly-Fourier curve for parameter-efficient motion encoding. We demonstrate that MOSAIC-GS achieves substantially faster optimization and rendering compared to existing methods, while maintaining reconstruction quality on par with state-of-the-art approaches across standard monocular dynamic scene benchmarks.

Related papers

• SV-GS: Sparse View 4D Reconstruction with Skeleton-Driven Gaussian Splatting [19.12278036176021]
  We present SV-GS, a framework that simultaneously estimates a deformation model and the object's motion over time under sparse observations.<n>Our method outperforms existing approaches under sparse observations by up to 34% in PSNR.
  arXiv  Detail & Related papers  (2026-01-01T09:53:03Z)
• SplitGaussian: Reconstructing Dynamic Scenes via Visual Geometry Decomposition [14.381223353489062]
  We propose textbfSplitGaussian, a novel framework that explicitly decomposes scene representations into static and dynamic components.<n>SplitGaussian outperforms prior state-of-the-art methods in rendering quality, geometric stability, and motion separation.
  arXiv  Detail & Related papers  (2025-08-06T09:00:13Z)
• DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos [52.46386528202226]
  We introduce the Deformable Gaussian Splats Large Reconstruction Model (DGS-LRM)<n>It is the first feed-forward method predicting deformable 3D Gaussian splats from a monocular posed video of any dynamic scene.<n>It achieves performance on par with state-of-the-art monocular video 3D tracking methods.
  arXiv  Detail & Related papers  (2025-06-11T17:59:58Z)
• DynaSplat: Dynamic-Static Gaussian Splatting with Hierarchical Motion Decomposition for Scene Reconstruction [9.391616497099422]
  We present DynaSplat, an approach that extends Gaussian Splatting to dynamic scenes.<n>We classify scene elements as static or dynamic through a novel fusion of deformation offset statistics and 2D motion flow consistency.<n>We then introduce a hierarchical motion modeling strategy that captures both coarse global transformations and fine-grained local movements.
  arXiv  Detail & Related papers  (2025-06-11T15:13:35Z)
• Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos [110.3924779333809]
  We present BTimer, the first motion-aware feed-forward model for real-time reconstruction and novel view synthesis of dynamic scenes.<n>Our approach reconstructs the full scene in a 3D Gaussian Splatting representation at a given target ('bullet') timestamp by aggregating information from all the context frames.<n>Given a casual monocular dynamic video, BTimer reconstructs a bullet-time scene within 150ms while reaching state-of-the-art performance on both static and dynamic scene datasets.
  arXiv  Detail & Related papers  (2024-12-04T18:15:06Z)
• Event-boosted Deformable 3D Gaussians for Dynamic Scene Reconstruction [50.873820265165975]
  We introduce the first approach combining event cameras, which capture high-temporal-resolution, continuous motion data, with deformable 3D-GS for dynamic scene reconstruction.<n>We propose a GS-Threshold Joint Modeling strategy, creating a mutually reinforcing process that greatly improves both 3D reconstruction and threshold modeling.<n>We contribute the first event-inclusive 4D benchmark with synthetic and real-world dynamic scenes, on which our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-11-25T08:23:38Z)
• Adaptive and Temporally Consistent Gaussian Surfels for Multi-view Dynamic Reconstruction [3.9363268745580426]
  AT-GS is a novel method for reconstructing high-quality dynamic surfaces from multi-view videos through per-frame incremental optimization. We reduce temporal jittering in dynamic surfaces by ensuring consistency in curvature maps across consecutive frames. Our method achieves superior accuracy and temporal coherence in dynamic surface reconstruction, delivering high-fidelity space-time novel view synthesis.
  arXiv  Detail & Related papers  (2024-11-10T21:30:16Z)
• MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion [118.74385965694694]
  We present Motion DUSt3R (MonST3R), a novel geometry-first approach that directly estimates per-timestep geometry from dynamic scenes.<n>By simply estimating a pointmap for each timestep, we can effectively adapt DUST3R's representation, previously only used for static scenes, to dynamic scenes.<n>We show that by posing the problem as a fine-tuning task, identifying several suitable datasets, and strategically training the model on this limited data, we can surprisingly enable the model to handle dynamics.
  arXiv  Detail & Related papers  (2024-10-04T18:00:07Z)
• Modeling Ambient Scene Dynamics for Free-view Synthesis [31.233859111566613]
  We introduce a novel method for dynamic free-view synthesis of an ambient scenes from a monocular capture. Our method builds upon the recent advancements in 3D Gaussian Splatting (3DGS) that can faithfully reconstruct complex static scenes.
  arXiv  Detail & Related papers  (2024-06-13T17:59:11Z)
• Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-time Rendering [49.36767999382054]
  We present a unified representation model, called Periodic Vibration Gaussian (PVG)<n>PVG builds upon the efficient 3D Gaussian splatting technique, originally designed for static scene representation.<n>PVG exhibits 900-fold acceleration in rendering over the best alternative.
  arXiv  Detail & Related papers  (2023-11-30T13:53:50Z)
• SceNeRFlow: Time-Consistent Reconstruction of General Dynamic Scenes [75.9110646062442]
  We propose SceNeRFlow to reconstruct a general, non-rigid scene in a time-consistent manner. Our method takes multi-view RGB videos and background images from static cameras with known camera parameters as input. We show experimentally that, unlike prior work that only handles small motion, our method enables the reconstruction of studio-scale motions.
  arXiv  Detail & Related papers  (2023-08-16T09:50:35Z)

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.