SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2506.03594v1
• Date: Wed, 04 Jun 2025 05:53:16 GMT
• Title: SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting
• Authors: Shengjie Lin, Jiading Fang, Muhammad Zubair Irshad, Vitor Campagnolo Guizilini, Rares Andrei Ambrus, Greg Shakhnarovich, Matthew R. Walter,
• Abstract summary: 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.
• Score: 15.098827709119087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reconstructing articulated objects prevalent in daily environments is crucial for applications in augmented/virtual reality and robotics. However, existing methods face scalability limitations (requiring 3D supervision or costly annotations), robustness issues (being susceptible to local optima), and rendering shortcomings (lacking speed or photorealism). We introduce SplArt, a self-supervised, category-agnostic framework that leverages 3D Gaussian Splatting (3DGS) to reconstruct articulated objects and infer kinematics from two sets of posed RGB images captured at different articulation states, enabling real-time photorealistic rendering for novel viewpoints and articulations. SplArt augments 3DGS with a differentiable mobility parameter per Gaussian, achieving refined part segmentation. A multi-stage optimization strategy is employed to progressively handle reconstruction, part segmentation, and articulation estimation, significantly enhancing robustness and accuracy. SplArt exploits geometric self-supervision, effectively addressing challenging scenarios without requiring 3D annotations or category-specific priors. 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. Code is publicly available at https://github.com/ripl/splart.

Related papers

• 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)
• GSemSplat: Generalizable Semantic 3D Gaussian Splatting from Uncalibrated Image Pairs [33.74118487769923]
  We introduce GSemSplat, a framework that learns semantic representations linked to 3D Gaussians without per-scene optimization, dense image collections or calibration.<n>We employ a dual-feature approach that leverages both region-specific and context-aware semantic features as supervision in the 2D space.
  arXiv  Detail & Related papers  (2024-12-22T09:06:58Z)
• 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)
• GSplatLoc: Grounding Keypoint Descriptors into 3D Gaussian Splatting for Improved Visual Localization [1.4466437171584356]
  We propose a two-stage procedure that integrates dense and robust keypoint descriptors from the lightweight XFeat feature extractor into 3DGS.<n>In the second stage, the initial pose estimate is refined by minimizing the rendering-based photometric warp loss.<n> Benchmarking on widely used indoor and outdoor datasets demonstrates improvements over recent neural rendering-based localization methods.
  arXiv  Detail & Related papers  (2024-09-24T23:18:32Z)
• SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction [24.33543853742041]
  3D Gaussian Splatting (3DGS) has emerged as a practical and scalable reconstruction method. We propose an optimization strategy that effectively regularizes splat features by modeling them as the outputs of a corresponding implicit neural field. Our approach effectively handles static and dynamic cases, as demonstrated by extensive testing across different setups and scene complexities.
  arXiv  Detail & Related papers  (2024-09-17T14:04:20Z)
• 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)
• SAGS: Structure-Aware 3D Gaussian Splatting [53.6730827668389]
  We propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene. SAGS reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets.
  arXiv  Detail & Related papers  (2024-04-29T23:26:30Z)
• InstantSplat: Sparse-view Gaussian Splatting in Seconds [91.77050739918037]
  We introduce InstantSplat, a novel approach for addressing sparse-view 3D scene reconstruction at lightning-fast speed.<n>InstantSplat employs a self-supervised framework that optimize 3D scene representation and camera poses.<n>It achieves an acceleration of over 30x in reconstruction and improves visual quality (SSIM) from 0.3755 to 0.7624 compared to traditional SfM with 3D-GS.
  arXiv  Detail & Related papers  (2024-03-29T17:29:58Z)
• HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting [53.6394928681237]
  holistic understanding of urban scenes based on RGB images is a challenging yet important problem. Our main idea involves the joint optimization of geometry, appearance, semantics, and motion using a combination of static and dynamic 3D Gaussians. Our approach offers the ability to render new viewpoints in real-time, yielding 2D and 3D semantic information with high accuracy.
  arXiv  Detail & Related papers  (2024-03-19T13:39:05Z)
• GGRt: Towards Pose-free Generalizable 3D Gaussian Splatting in Real-time [112.32349668385635]
  GGRt is a novel approach to generalizable novel view synthesis that alleviates the need for real camera poses. As the first pose-free generalizable 3D-GS framework, GGRt achieves inference at $ge$ 5 FPS and real-time rendering at $ge$ 100 FPS.
  arXiv  Detail & Related papers  (2024-03-15T09:47:35Z)

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.