FreeArtGS: 自由移動シナリオにおける関節物体のガウシアンスプラッティング再構成
FreeArtGS: Articulated Gaussian Splatting Under Free-moving Scenario
単眼RGB-Dビデオから自由に動く関節物体を再構成する新しい手法を提案。部品分割と関節推定を組み合わせ、エンドツーエンドで最適化する。
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著者: Hang Dai, Hongwei Fan, Han Zhang, Duojin Wu, Jiyao Zhang, Hao Dong
分類: cs.CV, cs.GR, cs.RO
原文アブストラクト
The increasing demand for augmented reality and robotics is driving the need for articulated object reconstruction with high scalability. However, existing settings for reconstructing from discrete articulation states or casual monocular videos require non-trivial axis alignment or suffer from insufficient coverage, limiting their applicability. In this paper, we introduce FreeArtGS, a novel method for reconstructing articulated objects under free-moving scenario, a new setting with a simple setup and high scalability. FreeArtGS combines free-moving part segmentation with joint estimation and end-to-end optimization, taking only a monocular RGB-D video as input. By optimizing with the priors from off-the-shelf point-tracking and feature models, the free-moving part segmentation module identifies rigid parts from relative motion under unconstrained capture. The joint estimation module calibrates the unified object-to-camera poses and recovers joint type and axis robustly from part segmentation. Finally, 3DGS-based end-to-end optimization is implemented to jointly reconstruct visual textures, geometry, and joint angles of the articulated object. We conduct experiments on two benchmarks and real-world free-moving articulated objects. Experimental results demonstrate that FreeArtGS consistently excels in reconstructing free-moving articulated objects and remains highly competitive in previous reconstruction settings, proving itself a practical and effective solution for realistic asset generation. The project page is available at: https://freeartgs.github.io/
