ChronoGS: Disentangling Invariants and Changes in Multi-Period Scenes

• URL: http://arxiv.org/abs/2511.18794v1
• Date: Mon, 24 Nov 2025 05:55:33 GMT
• Title: ChronoGS: Disentangling Invariants and Changes in Multi-Period Scenes
• Authors: Zhongtao Wang, Jiaqi Dai, Qingtian Zhu, Yilong Li, Mai Su, Fei Zhu, Meng Gai, Shaorong Wang, Chengwei Pan, Yisong Chen, Guoping Wang,
• Abstract summary: We introduce ChronoGS, a temporally modulated Gaussian representation that reconstructs all periods within a unified anchor scaffold.<n> Experiments demonstrate that ChronoGS consistently outperforms baselines in reconstruction quality and temporal consistency.
• Score: 21.94404265718666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-period image collections are common in real-world applications. Cities are re-scanned for mapping, construction sites are revisited for progress tracking, and natural regions are monitored for environmental change. Such data form multi-period scenes, where geometry and appearance evolve. Reconstructing such scenes is an important yet underexplored problem. Existing pipelines rely on incompatible assumptions: static and in-the-wild methods enforce a single geometry, while dynamic ones assume smooth motion, both failing under long-term, discontinuous changes. To solve this problem, we introduce ChronoGS, a temporally modulated Gaussian representation that reconstructs all periods within a unified anchor scaffold. It's also designed to disentangle stable and evolving components, achieving temporally consistent reconstruction of multi-period scenes. To catalyze relevant research, we release ChronoScene dataset, a benchmark of real and synthetic multi-period scenes, capturing geometric and appearance variation. Experiments demonstrate that ChronoGS consistently outperforms baselines in reconstruction quality and temporal consistency. Our code and the ChronoScene dataset are publicly available at https://github.com/ZhongtaoWang/ChronoGS.

Related papers

• Long-Term Multi-Session 3D Reconstruction Under Substantial Appearance Change [52.46888249268445]
  Long-term environmental monitoring requires the ability to reconstruct and align 3D models across repeated site visits separated by months or years.<n>Existing approaches rely on post-hoc alignment of independently reconstructed sessions.<n>We propose enforcing cross-session correspondences directly within a joint SfM reconstruction.
  arXiv  Detail & Related papers  (2026-02-24T06:12:51Z)
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories [78.78355829813793]
  Existing memory-based approaches often condition generation on globally reconstructed 3D scenes by rendering anchor videos from the reconstructed geometry in the history.<n>We introduce AnchorWeave, a memory-augmented video generation framework that replaces a single misaligned global memory with multiple clean local geometric memories.<n>Experiments demonstrate that AnchorWeave significantly improves long-term scene consistency while maintaining strong visual quality.
  arXiv  Detail & Related papers  (2026-02-16T17:23:08Z)
• TimeMar: Multi-Scale Autoregressive Modeling for Unconditional Time Series Generation [11.455232661227313]
  We propose a structure-disentangled multiscale generation framework for time series.<n>Our approach encodes sequences into discrete tokens at multiple temporal resolutions.<n>We show that our approach produces higher-quality time series than existing methods.
  arXiv  Detail & Related papers  (2026-01-16T11:00:05Z)
• LTGS: Long-Term Gaussian Scene Chronology From Sparse View Updates [20.57134931668659]
  Long-term Gaussian scene chronology from sparse-view updates is proposed.<n>We robustly model the long-term chronology of the scene despite abrupt movements and subtle environmental variations.<n>Our framework is generalizable across multiple time steps through simple transformations.
  arXiv  Detail & Related papers  (2025-10-10T21:36:49Z)
• Kairos: Towards Adaptive and Generalizable Time Series Foundation Models [27.076542021368056]
  Time series foundation models (TSFMs) have emerged as a powerful paradigm for time series analysis.<n>We propose Kairos, a flexible TSFM framework that integrates a dynamic patching tokenizer and an instance-adaptive positional embedding.<n>Kairos achieves superior performance with much fewer parameters on two common zero-shot benchmarks.
  arXiv  Detail & Related papers  (2025-09-30T06:02:26Z)
• STD-GS: Exploring Frame-Event Interaction for SpatioTemporal-Disentangled Gaussian Splatting to Reconstruct High-Dynamic Scene [54.418259038624406]
  existing methods adopt unified representation model (e.g. Gaussian) to directly match scene from frame camera.<n>However, this unified paradigm fails in the potential temporal features of objects due to frame features and discontinuous spatial features between background and objects.<n>In this work, we introduce event camera to compensate for frame camera, and propose a distemporal-dentangle Gaussian splatting framework for high-dynamic scene reconstruction.
  arXiv  Detail & Related papers  (2025-06-29T09:32:06Z)
• Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments [20.890476387720483]
  MoRE is a novel approach for multi-object relocalization and reconstruction in evolving environments. We view these environments as "living scenes" and consider the problem of transforming scans taken at different points in time into a 3D reconstruction of the object instances.
  arXiv  Detail & Related papers  (2023-12-14T17:09:57Z)
• Nothing Stands Still: A Spatiotemporal Benchmark on 3D Point Cloud Registration Under Large Geometric and Temporal Change [82.31647863785923]
  Building 3D geometric maps of man-made spaces are fundamental computer vision and robotics.<n>Nothing Stands Still (NSS) benchmark focuses on thetemporal registration of 3D scenes undergoing large spatial and temporal change.<n>As part of NSS, we introduce a dataset of 3D point clouds recurrently captured in large-scale building indoor environments that are under construction or renovation.
  arXiv  Detail & Related papers  (2023-11-15T20:09:29Z)
• Neural Scene Chronology [79.51094408119148]
  We aim to reconstruct a time-varying 3D model, capable of rendering photo-realistic renderings with independent control of viewpoint, illumination, and time. In this work, we represent the scene as a space-time radiance field with a per-image illumination embedding, where temporally-varying scene changes are encoded using a set of learned step functions.
  arXiv  Detail & Related papers  (2023-06-13T17:59:58Z)
• Temporally Consistent Transformers for Video Generation [80.45230642225913]
  To generate accurate videos, algorithms have to understand the spatial and temporal dependencies in the world. No established benchmarks on complex data exist for rigorously evaluating video generation with long temporal dependencies. We introduce the Temporally Consistent Transformer (TECO), a generative model that substantially improves long-term consistency while also reducing sampling time.
  arXiv  Detail & Related papers  (2022-10-05T17:15:10Z)
• TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis [80.56913334060404]
  Time series analysis is of immense importance in applications, such as weather forecasting, anomaly detection, and action recognition. Previous methods attempt to accomplish this directly from the 1D time series. We ravel out the complex temporal variations into the multiple intraperiod- and interperiod-variations.
  arXiv  Detail & Related papers  (2022-10-05T12:19:51Z)

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.