基礎特徴量の幾何誘導モデリングによる一般化可能な物体形状変形学習
Geometry-Guided Modeling of Foundation Features Enables Generalizable Object Shape Deformation Learning
単眼画像から3D物体形状を復元するため、カテゴリレベルのテンプレートを変形させる枠組みを提案し、基礎特徴量にテンプレートのトポロジーを組み込む幾何誘導機構と視点適応型特徴集約により、未知カテゴリや多視点への頑健な一般化を実現した。
著者: Yiyao Ma, Kai Chen, Zhongxiang Zhou, Zhuheng Song, Dongsheng Xie, Zelong Tan, Rong Xiong, Qi Dou
分類: cs.CV
原文アブストラクト
Monocular 3D shape recovery is fundamental to geometric understanding, yet achieving robust generalization across arbitrary viewpoints and unseen object categories remains a significant challenge. In this paper, we present a generalizable deformation learning framework that reconstructs 3D objects by explicitly deforming a category-level shape template to match the target observation. To address complex shape variations between the template and the target, we introduce a geometry-guided feature modeling mechanism. This process first enriches foundation features with template topology to yield a geometry-aware representation, which is then explicitly correlated with the target observation to guide precise deformation. Furthermore, to bridge the disparity between the fixed template and arbitrary target views, we propose a view-adaptive feature aggregation module. This module leverages multi-view template features and their corresponding camera poses to enrich the canonical template representation, ensuring robust feature alignment regardless of the target's perspective. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods in handling large shape variations and diverse viewpoints, exhibiting strong generalization to novel categories and effectively supporting downstream real-world dexterous robotic manipulation tasks. Project homepage: https://GODeform.github.io/