GS-LTS: 3D Gaussian Splatting-Based Adaptive Modeling for Long-Term Service Robots
- URL: http://arxiv.org/abs/2503.17733v1
- Date: Sat, 22 Mar 2025 11:26:47 GMT
- Title: GS-LTS: 3D Gaussian Splatting-Based Adaptive Modeling for Long-Term Service Robots
- Authors: Bin Fu, Jialin Li, Bin Zhang, Ruiping Wang, Xilin Chen,
- Abstract summary: 3D Gaussian Splatting (3DGS) has garnered significant attention in robotics for its explicit, high fidelity dense scene representation.<n>We propose GS-LTS (Gaussian Splatting for Long-Term Service), a 3DGS-based system enabling indoor robots to manage diverse tasks in dynamic environments over time.
- Score: 33.19663755125912
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 3D Gaussian Splatting (3DGS) has garnered significant attention in robotics for its explicit, high fidelity dense scene representation, demonstrating strong potential for robotic applications. However, 3DGS-based methods in robotics primarily focus on static scenes, with limited attention to the dynamic scene changes essential for long-term service robots. These robots demand sustained task execution and efficient scene updates-challenges current approaches fail to meet. To address these limitations, we propose GS-LTS (Gaussian Splatting for Long-Term Service), a 3DGS-based system enabling indoor robots to manage diverse tasks in dynamic environments over time. GS-LTS detects scene changes (e.g., object addition or removal) via single-image change detection, employs a rule-based policy to autonomously collect multi-view observations, and efficiently updates the scene representation through Gaussian editing. Additionally, we propose a simulation-based benchmark that automatically generates scene change data as compact configuration scripts, providing a standardized, user-friendly evaluation benchmark. Experimental results demonstrate GS-LTS's advantages in reconstruction, navigation, and superior scene updates-faster and higher quality than the image training baseline-advancing 3DGS for long-term robotic operations. Code and benchmark are available at: https://vipl-vsu.github.io/3DGS-LTS.
Related papers
- 3DGS_LSR:Large_Scale Relocation for Autonomous Driving Based on 3D Gaussian Splatting [3.2768514034762863]
3DGS-LSR: a large-scale relocalization framework leveraging 3DGS, enabling centimeter-level positioning using only a single monocular RGB image on the client side.<n>We combine multi-sensor data to construct high-accuracy 3DGS maps in large outdoor scenes, while the robot-side localization requires just a standard camera input.<n>Our core innovation is an iterative optimization strategy that refines localization results through step-by-step rendering, making it suitable for real-time autonomous navigation.
arXiv Detail & Related papers (2025-07-08T04:43:46Z) - CL-Splats: Continual Learning of Gaussian Splatting with Local Optimization [68.89159693946685]
This paper introduces CL-Splats, which incrementally updates 3D representations from sparse scene captures.<n> CL-Splats integrates a robust change-detection module that segments updated and static components within the scene.<n>Our experiments demonstrate that CL-Splats achieves efficient updates with improved reconstruction quality over the state-of-the-art.
arXiv Detail & Related papers (2025-06-26T09:32:37Z) - 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) - SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting [15.098827709119087]
We introduce SplArt, a self-supervised, category-agnostic framework to reconstruct articulated objects and infer kinematics from two sets of posed RGB images.<n>SplArt exploits geometric self-supervision, effectively addressing challenging scenarios without requiring 3D annotations or category-specific priors.<n> Evaluations on established and newly proposed benchmarks, along with applications to real-world scenarios using a handheld RGB camera, demonstrate SplArt's state-of-the-art performance and real-world practicality.
arXiv Detail & Related papers (2025-06-04T05:53:16Z) - EGSRAL: An Enhanced 3D Gaussian Splatting based Renderer with Automated Labeling for Large-Scale Driving Scene [19.20846992699852]
We propose EGSRAL, a 3D GS-based method that relies solely on training images without extra annotations.
EGSRAL enhances 3D GS's capability to model both dynamic objects and static backgrounds.
We also propose a grouping strategy for vanilla 3D GS to address perspective issues in rendering large-scale, complex scenes.
arXiv Detail & Related papers (2024-12-20T04:21:54Z) - SparseGrasp: Robotic Grasping via 3D Semantic Gaussian Splatting from Sparse Multi-View RGB Images [125.66499135980344]
We propose SparseGrasp, a novel open-vocabulary robotic grasping system.<n>SparseGrasp operates efficiently with sparse-view RGB images and handles scene updates fastly.<n>We show that SparseGrasp significantly outperforms state-of-the-art methods in terms of both speed and adaptability.
arXiv Detail & Related papers (2024-12-03T03:56:01Z) - T-3DGS: Removing Transient Objects for 3D Scene Reconstruction [83.05271859398779]
Transient objects in video sequences can significantly degrade the quality of 3D scene reconstructions.<n>We propose T-3DGS, a novel framework that robustly filters out transient distractors during 3D reconstruction using Gaussian Splatting.
arXiv Detail & Related papers (2024-11-29T07:45:24Z) - Next Best Sense: Guiding Vision and Touch with FisherRF for 3D Gaussian Splatting [27.45827655042124]
We propose a framework for active next best view and touch selection for robotic manipulators using 3D Gaussian Splatting (3DGS)<n>We first elevate the performance of few-shot 3DGS with a novel semantic depth alignment method.<n>We then extend FisherRF, a next-best-view selection method for 3DGS, to select views and touch poses based on depth uncertainty.
arXiv Detail & Related papers (2024-10-07T01:24:39Z) - Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion [54.197343533492486]
Event3DGS can reconstruct high-fidelity 3D structure and appearance under high-speed egomotion.
Experiments on multiple synthetic and real-world datasets demonstrate the superiority of Event3DGS compared with existing event-based dense 3D scene reconstruction frameworks.
Our framework also allows one to incorporate a few motion-blurred frame-based measurements into the reconstruction process to further improve appearance fidelity without loss of structural accuracy.
arXiv Detail & Related papers (2024-06-05T06:06:03Z) - SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation [62.58480650443393]
Segment Anything (SAM) is a vision-foundation model for generalizable scene understanding and sequence imitation.
We develop a novel multi-channel heatmap that enables the prediction of the action sequence in a single pass.
arXiv Detail & Related papers (2024-05-30T00:32:51Z) - 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) - CoGS: Controllable Gaussian Splatting [5.909271640907126]
Controllable Gaussian Splatting (CoGS) is a new method for capturing and re-animating 3D structures.
CoGS offers real-time control of dynamic scenes without the prerequisite of pre-computing control signals.
In our evaluations, CoGS consistently outperformed existing dynamic and controllable neural representations in terms of visual fidelity.
arXiv Detail & Related papers (2023-12-09T20:06:29Z) - SG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs [81.15889805560333]
We present SG-Bot, a novel rearrangement framework.
SG-Bot exemplifies lightweight, real-time, and user-controllable characteristics.
Experimental results demonstrate that SG-Bot outperforms competitors by a large margin.
arXiv Detail & Related papers (2023-09-21T15:54:33Z) - 3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable
Scene Graphs [29.898086255614484]
We formalize the task of semantic scene variability estimation.
We identify three main varieties of semantic scene change: changes in the position of an object, its semantic state, or the composition of a scene as a whole.
We present a novel method, DeltaVSG, to estimate the variability of VSGs in a supervised fashion.
arXiv Detail & Related papers (2022-09-16T12:41:43Z)
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.