Stipple: Real-Time Incremental Gaussian Splatting with Visual-Inertial Tracking

• URL: http://arxiv.org/abs/2608.00931v1
• Date: Sun, 02 Aug 2026 02:17:05 GMT
• Title: Stipple: Real-Time Incremental Gaussian Splatting with Visual-Inertial Tracking
• Abstract summary: We propose a new method that tracks and reconstructs simultaneously in real-time by leveraging an efficient visual-inertial tracking system.<n>We show that many of the heavy preprocessing and training steps of 3DGS can be replaced with a more efficient incremental training strategy.
• Score: 45.88028371034407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) provides efficient rendering of photo-realistic scenes, but its heavy preprocessing and training steps make it a poor fit for applications that require real-time reconstruction in robotics or XR. This capability is important since it allows immediate feedback and interaction with new environments. Visual-inertial odometry (VIO) and simultaneous localization and mapping (VI-SLAM) systems, on the other hand, specifically target these real-time applications, which makes them a good choice for integration with 3DGS. We propose a new method that tracks and reconstructs simultaneously in real-time by leveraging an efficient visual-inertial tracking system based on Basalt together with a novel incremental method built on top of Brush, an efficient Rust-based GPU-vendor-agnostic implementation of 3D Gaussian Splatting. We show that many of the heavy preprocessing and training steps of 3DGS can be replaced with a more efficient incremental training strategy that has direct access to the information generated by the visual-inertial tracking system. Furthermore, we propose and combine multiple practical improvements to increase the efficiency of the training pipeline and adapt it to run in real-time, parallel to the tracking thread. This work highlights the value of exploiting the complementary nature of SLAM and 3DGS, and how that can lead to promising results for real-time 3D reconstruction.

Related papers

• Geometry-Aware Online Mapping for 3D Gaussian Splatting SLAM [1.988537471957171]
  Recent 3D Gaussian Splatting (3DGS) has enabled efficient view computation and is rapidly being adopted in simultaneous localization and mapping (SLAM) systems for online mapping.<n>In this work, we propose three geometry-aware methods that operate in a decoupled 3DGS-SLAM setting.<n>Our results show consistent improvements in rendering quality with negligible overhead, highlighting the coupling between photometric residuals and pose uncertainty in online SLAM.
  arXiv  Detail & Related papers  (2026-08-14T21:20:16Z)
• TrackerSplat: Exploiting Point Tracking for Fast and Robust Dynamic 3D Gaussians Reconstruction [13.65229165812888]
  We introduce textitTrackerSplat, a novel method that integrates advanced point tracking methods to enhance robustness and scalability of 3DGS for dynamic scene reconstruction.<n>TrackerSplat effectively handles large displacements between frames, dramatically reducing the fading and recoloring artifacts prevalent in prior methods.<n> Experiments on real-world datasets confirm the robustness of TrackerSplat in challenging scenarios with significant displacements.
  arXiv  Detail & Related papers  (2026-04-02T23:43:55Z)
• FeatureSLAM: Feature-enriched 3D gaussian splatting SLAM in real time [11.883404434697809]
  We present a real-time tracking SLAM system that unifies efficient camera tracking and feature-enriched mapping using 3D Gaussian Splatting (3DGS)<n>Our main contribution is integrating dense feature semanticization with semanticization into the novel-view synthesis aligned with a visual foundation model.<n>We obtain 9% lower pose error and 8% higher mapping accuracy compared to recent fixed-set SLAM baselines.
  arXiv  Detail & Related papers  (2026-01-09T11:40:16Z)
• Pseudo Depth Meets Gaussian: A Feed-forward RGB SLAM Baseline [64.42938561167402]
  We propose an online 3D reconstruction method using 3D Gaussian-based SLAM, combined with a feed-forward recurrent prediction module.<n>This approach replaces slow test-time optimization with fast network inference, significantly improving tracking speed.<n>Our method achieves performance on par with the state-of-the-art SplaTAM, while reducing tracking time by more than 90%.
  arXiv  Detail & Related papers  (2025-08-06T16:16:58Z)
• EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis [61.1662426227688]
  Existing NeRF and 3DGS-based methods show promising results in achieving photorealistic renderings but require slow, per-scene optimization.<n>We introduce EVolSplat, an efficient 3D Gaussian Splatting model for urban scenes that works in a feed-forward manner.
  arXiv  Detail & Related papers  (2025-03-26T02:47:27Z)
• Towards Real-Time Gaussian Splatting: Accelerating 3DGS through Photometric SLAM [4.08109886949724]
  We propose integrating 3DGS with Direct Sparse Odometry, a monocular photometric SLAM system. Preliminary experiments show that using Direct Sparse Odometry point cloud outputs, as opposed to standard structure-from-motion methods, significantly shortens the training time needed to achieve high-quality renders.
  arXiv  Detail & Related papers  (2024-08-07T15:01:08Z)
• 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)
• LP-3DGS: Learning to Prune 3D Gaussian Splatting [71.97762528812187]
  We propose learning-to-prune 3DGS, where a trainable binary mask is applied to the importance score that can find optimal pruning ratio automatically. Experiments have shown that LP-3DGS consistently produces a good balance that is both efficient and high quality.
  arXiv  Detail & Related papers  (2024-05-29T05:58:34Z)
• MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements [59.70107451308687]
  We show for the first time that using 3D Gaussians for map representation with unposed camera images and inertial measurements can enable accurate SLAM. Our method, MM3DGS, addresses the limitations of prior rendering by enabling faster scale awareness, and improved trajectory tracking. We also release a multi-modal dataset, UT-MM, collected from a mobile robot equipped with a camera and an inertial measurement unit.
  arXiv  Detail & Related papers  (2024-04-01T04:57:41Z)
• GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting [51.96353586773191]
  We introduce textbfGS-SLAM that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping system. Our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica, TUM-RGBD datasets.
  arXiv  Detail & Related papers  (2023-11-20T12:08:23Z)

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.