ArtPro: Self-Supervised Articulated Object Reconstruction with Adaptive Integration of Mobility Proposals

TL;DR

This work proposes ArtPro, a novel self-supervised framework that introduces adaptive integration of mobility proposals and achieves robust reconstruction of complex multi-part objects, significantly outperforming existing methods in accuracy and stability.

Abstract

Reconstructing articulated objects into high-fidelity digital twins is crucial for applications such as robotic manipulation and interactive simulation. Recent self-supervised methods using differentiable rendering frameworks like 3D Gaussian Splatting remain highly sensitive to the initial part segmentation. Their reliance on heuristic clustering or pre-trained models often causes optimization to converge to local minima, especially for complex multi-part objects. To address these limitations, we propose ArtPro, a novel self-supervised framework that introduces adaptive integration of mobility proposals. Our approach begins with an over-segmentation initialization guided by geometry features and motion priors, generating part proposals with plausible motion hypotheses. During optimization, we dynamically merge these proposals by analyzing motion consistency among spatial neighbors, while a collision-aware motion pruning mechanism prevents erroneous kinematic estimation. Extensive experiments on both synthetic and real-world objects demonstrate that ArtPro achieves robust reconstruction of complex multi-part objects, significantly outperforming existing methods in accuracy and stability.

Figures (13)

• Figure 1: Existing methods like ArtGS liu2025artgs are highly sensitive to initial part segmentation, leading to inaccurate motion and geometry. Our method, ArtPro, leverages a prior-guided mobility initialization and adaptively merges mobility proposals during optimization, achieving robust reconstruction of complex multi-part articulated objects.
• Figure 2: ArtPro reconstructs articulated objects from multi-view RGBD images of two states. The pipeline begins by initializing part-motion proposals through over-segmentation and a mixed-variable search. These proposals are then refined via a self-supervised optimization that updates the parts and their motion parameters using transformable 3D Gaussians. The optimization incorporates motion pruning and proposal integration operations to calibrate motions and merge proposals into coherent movable parts. Finally, a post-processing refinement stabilizes the appearance, geometry, and motion parameters of the reconstruction.
• Figure 3: The part initialization and reconstruction results of ArtGS liu2025artgs and ours on two-part objects. Although our approach doesn't merge the two disadjacent movable parts (second row), we still obtain accurate motion and geometry reconstruction.
• Figure 4: The reconstructed articulated objects in different motion states ($t=\{0, 0.5, 1\}$) and their part-motion structures.
• Figure 5: The intermediate results during our adaptive proposal integration optimization. We show the optimized Gaussians (with their center points) and the estimated motions before the proposal integration operation at each cycle.