RoboArmGS: ベジェ曲線補正による高品質なロボットアームのスプラッティング
RoboArmGS: High-Quality Robotic Arm Splatting via B\'ezier Curve Refinement
URDFに基づく理想的な関節運動を学習可能なベジェ曲線で補正し、実世界の動きに即した高品質な3Dガウシアン表現を実現した研究。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Hao Wang, Xiaobao Wei, Ying Li, Qingpo Wuwu, Dongli Wu, Jiajun Cao, Ming Lu, Wenzhao Zheng, Shanghang Zhang
分類: cs.RO
原文アブストラクト
Constructing photorealistic and controllable robotic arm digital assets from real observations is fundamental to robotic applications. Current approaches naively bind static 3D Gaussians according to URDF links, forcing them to follow an URDF-rigged motion passively. However, the idealized URDF-rigged motion cannot accurately model the actual motion captured in real-world observations, leading to severe rendering artifacts in 3D Gaussians. To address these challenges, we propose RoboArmGS, a novel hybrid representation that refines the URDF-rigged motion with learnable B\'ezier curves, enabling more accurate real-world motion modeling. To be more specific, we present a learnable B\'ezier Curve motion refiner that corrects per-joint residuals to address mismatches between real-world motion and URDF-rigged motion. RoboArmGS enables the learning of more accurate real-world motion while achieving a coherent binding of 3D Gaussians across arm parts. To support future research, we contribute a carefully collected dataset named RoboArm4D, which comprises several widely used robotic arms for evaluating the quality of building high-quality digital assets. We evaluate our approach on RoboArm4D, and RoboArmGS achieves state-of-the-art performance in real-world motion modeling and rendering quality. The code and dataset will be released.
