Dynamic Visual SLAM using a General 3D Prior

• URL: http://arxiv.org/abs/2512.06868v1
• Date: Sun, 07 Dec 2025 14:44:06 GMT
• Title: Dynamic Visual SLAM using a General 3D Prior
• Authors: Xingguang Zhong, Liren Jin, Marija Popović, Jens Behley, Cyrill Stachniss,
• Abstract summary: We propose a novel monocular visual SLAM system that can robustly estimate camera poses in dynamic scenes.<n> Specifically, we propose a feed-forward reconstruction model to precisely filter out dynamic regions.
• Score: 27.487134809452147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable incremental estimation of camera poses and 3D reconstruction is key to enable various applications including robotics, interactive visualization, and augmented reality. However, this task is particularly challenging in dynamic natural environments, where scene dynamics can severely deteriorate camera pose estimation accuracy. In this work, we propose a novel monocular visual SLAM system that can robustly estimate camera poses in dynamic scenes. To this end, we leverage the complementary strengths of geometric patch-based online bundle adjustment and recent feed-forward reconstruction models. Specifically, we propose a feed-forward reconstruction model to precisely filter out dynamic regions, while also utilizing its depth prediction to enhance the robustness of the patch-based visual SLAM. By aligning depth prediction with estimated patches from bundle adjustment, we robustly handle the inherent scale ambiguities of the batch-wise application of the feed-forward reconstruction model.

Related papers

• JOintGS: Joint Optimization of Cameras, Bodies and 3D Gaussians for In-the-Wild Monocular Reconstruction [18.636227266388218]
  We present JOintGS, a unified framework that jointly optimize camera extrinsics, human poses, and 3D Gaussian representations.<n>Experiments on NeuMan and EMDB datasets demonstrate that JOintGS achieves superior reconstruction quality.
  arXiv  Detail & Related papers  (2026-02-04T08:33:51Z)
• 4D3R: Motion-Aware Neural Reconstruction and Rendering of Dynamic Scenes from Monocular Videos [52.89084603734664]
  We present 4D3R, a pose-free dynamic neural rendering framework that decouples static and dynamic components through a two-stage approach.<n>Our approach achieves up to 1.8dB PSNR improvement over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-11-07T13:25:50Z)
• PAGE-4D: Disentangled Pose and Geometry Estimation for 4D Perception [39.819707648812944]
  PAGE-4D is a feedforward model that extends VGGT to dynamic scenes without post-processing.<n>It disentangles static and dynamic information by predicting a dynamics-aware mask.<n>Experiments show that PAGE-4D consistently outperforms the original VGGT in dynamic scenarios.
  arXiv  Detail & Related papers  (2025-10-20T14:17:16Z)
• 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)
• Back on Track: Bundle Adjustment for Dynamic Scene Reconstruction [86.099855111676]
  Traditional SLAM systems struggle with highly dynamic scenes commonly found in casual videos.<n>This work leverages a 3D point tracker to separate the camera-induced motion from the observed motion of dynamic objects.<n>Our framework combines the core of traditional SLAM -- bundle adjustment -- with a robust learning-based 3D tracker front-end.
  arXiv  Detail & Related papers  (2025-04-20T07:29:42Z)
• 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)
• SMORE: Simultaneous Map and Object REconstruction [66.66729715211642]
  We present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR.<n>We take a holistic perspective and optimize a compositional model of a dynamic scene that decomposes the world into rigidly-moving objects and the background.
  arXiv  Detail & Related papers  (2024-06-19T23:53:31Z)
• DynaMoN: Motion-Aware Fast and Robust Camera Localization for Dynamic Neural Radiance Fields [71.94156412354054]
  We propose Dynamic Motion-Aware Fast and Robust Camera Localization for Dynamic Neural Radiance Fields (DynaMoN)<n>DynaMoN handles dynamic content for initial camera pose estimation and statics-focused ray sampling for fast and accurate novel-view synthesis.<n>We extensively evaluate our approach on two real-world dynamic datasets, the TUM RGB-D dataset and the BONN RGB-D Dynamic dataset.
  arXiv  Detail & Related papers  (2023-09-16T08:46:59Z)
• R3D3: Dense 3D Reconstruction of Dynamic Scenes from Multiple Cameras [106.52409577316389]
  R3D3 is a multi-camera system for dense 3D reconstruction and ego-motion estimation. Our approach exploits spatial-temporal information from multiple cameras, and monocular depth refinement. We show that this design enables a dense, consistent 3D reconstruction of challenging, dynamic outdoor environments.
  arXiv  Detail & Related papers  (2023-08-28T17:13:49Z)
• Visual-Inertial Multi-Instance Dynamic SLAM with Object-level Relocalisation [14.302118093865849]
  We present a tightly-coupled visual-inertial object-level multi-instance dynamic SLAM system. It can robustly optimise for the camera pose, velocity, IMU biases and build a dense 3D reconstruction object-level map of the environment.
  arXiv  Detail & Related papers  (2022-08-08T17:13:24Z)

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.