ロボットポリシー学習における視点汎化のための効率的なカメラポーズ拡張
Efficient Camera Pose Augmentation for View Generalization in Robotic Policy Learning
3Dガウススプラッティングを用いて、少数の非校正画像から高品質な3Dシーンを再構成し、多様な合成視点を生成することで、視覚運動ポリシーの未知視点への汎化性能を向上させる手法を提案した。
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？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Sen Wang, Huaiyi Dong, Jingyi Tian, Jiayi Li, Zhuo Yang, Tongtong Cao, Anlin Chen, Shuang Wu, Le Wang, Sanping Zhou
分類: cs.RO
原文アブストラクト
Prevailing 2D-centric visuomotor policies exhibit a pronounced deficiency in novel view generalization, as their reliance on static observations hinders consistent action mapping across unseen views. In response, we introduce GenSplat, a feed-forward 3D Gaussian Splatting framework that facilitates view-generalized policy learning through novel view rendering. GenSplat employs a permutation-equivariant architecture to reconstruct high-fidelity 3D scenes from sparse, uncalibrated inputs in a single forward pass. To ensure structural integrity, we design a 3D-prior distillation strategy that regularizes the 3DGS optimization, preventing the geometric collapse typical of purely photometric supervision. By rendering diverse synthetic views from these stable 3D representations, we systematically augment the observational manifold during training. This augmentation forces the policy to ground its decisions in underlying 3D structures, thereby ensuring robust execution under severe spatial perturbations where baselines severely degrade.
