DLO-Splatting: Tracking Deformable Linear Objects Using 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2505.08644v2
• Date: Wed, 21 May 2025 12:32:36 GMT
• Title: DLO-Splatting: Tracking Deformable Linear Objects Using 3D Gaussian Splatting
• Authors: Holly Dinkel, Marcel Büsching, Alberta Longhini, Brian Coltin, Trey Smith, Danica Kragic, Mårten Björkman, Timothy Bretl,
• Abstract summary: DLO-Splatting is an algorithm for estimating the 3D shape of Deformable Linear Objects (DLOs) from multi-view RGB images and gripper state information.<n>The algorithm uses a position-based dynamics model with shape smoothness and rigidity dampening corrections to predict the object shape.<n>Experiments demonstrate promising results in a knot tying scenario, which is challenging for existing vision-only methods.
• Score: 19.080636951502623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents DLO-Splatting, an algorithm for estimating the 3D shape of Deformable Linear Objects (DLOs) from multi-view RGB images and gripper state information through prediction-update filtering. The DLO-Splatting algorithm uses a position-based dynamics model with shape smoothness and rigidity dampening corrections to predict the object shape. Optimization with a 3D Gaussian Splatting-based rendering loss iteratively renders and refines the prediction to align it with the visual observations in the update step. Initial experiments demonstrate promising results in a knot tying scenario, which is challenging for existing vision-only methods.

Related papers

• 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)
• Intern-GS: Vision Model Guided Sparse-View 3D Gaussian Splatting [95.61137026932062]
  Intern-GS is a novel approach to enhance the process of sparse-view Gaussian splatting.<n>We show that Intern-GS achieves state-of-the-art rendering quality across diverse datasets.
  arXiv  Detail & Related papers  (2025-05-27T05:17:49Z)
• 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)
• MVS-GS: High-Quality 3D Gaussian Splatting Mapping via Online Multi-View Stereo [9.740087094317735]
  We propose a novel framework for high-quality 3DGS modeling using an online multi-view stereo approach.<n>Our method estimates MVS depth using sequential frames from a local time window and applies comprehensive depth refinement techniques.<n> Experimental results demonstrate that our method outperforms state-of-the-art dense SLAM methods.
  arXiv  Detail & Related papers  (2024-12-26T09:20:04Z)
• RP-SLAM: Real-time Photorealistic SLAM with Efficient 3D Gaussian Splatting [22.76955981251234]
  RP-SLAM is a 3D Gaussian splatting-based vision SLAM method for monocular and RGB-D cameras.<n>It achieves state-of-the-art map rendering accuracy while ensuring real-time performance and model compactness.
  arXiv  Detail & Related papers  (2024-12-13T05:27:35Z)
• 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. 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)
• Mode-GS: Monocular Depth Guided Anchored 3D Gaussian Splatting for Robust Ground-View Scene Rendering [47.879695094904015]
  We present a novelview rendering algorithm, Mode-GS, for ground-robot trajectory datasets. Our approach is based on using anchored Gaussian splats, which are designed to overcome the limitations of existing 3D Gaussian splatting algorithms. Our method results in improved rendering performance, based on PSNR, SSIM, and LPIPS metrics, in ground scenes with free trajectory patterns.
  arXiv  Detail & Related papers  (2024-10-06T23:01:57Z)
• GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction [52.04103235260539]
  We present a diffusion model approach based on Gaussian Splatting representation for 3D object reconstruction from a single view. The model learns to generate 3D objects represented by sets of GS ellipsoids. The final reconstructed objects explicitly come with high-quality 3D structure and texture, and can be efficiently rendered in arbitrary views.
  arXiv  Detail & Related papers  (2024-07-05T03:43:08Z)
• 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)
• Learned Vertex Descent: A New Direction for 3D Human Model Fitting [64.04726230507258]
  We propose a novel optimization-based paradigm for 3D human model fitting on images and scans. Our approach is able to capture the underlying body of clothed people with very different body shapes, achieving a significant improvement compared to state-of-the-art. LVD is also applicable to 3D model fitting of humans and hands, for which we show a significant improvement to the SOTA with a much simpler and faster method.
  arXiv  Detail & Related papers  (2022-05-12T17:55:51Z)
• Iterative Optimisation with an Innovation CNN for Pose Refinement [17.752556490937092]
  In this work we propose an approach, namely an Innovation CNN, to object pose estimation refinement. Our approach improves initial pose estimation progressively by applying the Innovation CNN iteratively in a gradient descent framework. We evaluate our method on the popular LINEMOD and Occlusion LINEMOD datasets and obtain state-of-the-art performance on both datasets.
  arXiv  Detail & Related papers  (2021-01-22T00:12:12Z)

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.