仅用4张图无姿态信息，就能重建可动物体的三维结构
PAOLI: Pose-free Articulated Object Learning from Sparse-view Images
- 先独立重建各关节，再通过变形场建立跨姿态对应关系
- 在仅四视图无姿态条件下实现高精度物体建模
- 适合缺乏相机位姿数据的实物扫描与逆向建模场景
我们提出一种新方法，仅需少量视角图像（每关节最少4张）且无需已知相机位姿，即可建模可动物体。现有方法依赖密集多视角观测和真实相机姿态。本方法首先利用最新的稀疏视角3D重建技术，独立重建每个关节；随后学习一个形变场，在不同姿态间建立稠密对应关系。采用渐进式解耦策略分离静态与运动部件，实现相机运动与物体运动的鲁棒分离。最后通过自监督损失联合优化几何、外观与运动学，强制保证跨视角和跨姿态的一致性。在标准基准和真实场景上的实验表明，该方法在远弱于现有方法输入条件的情况下，仍能生成精确且细节丰富的可动物体表示。
原文摘要 · Abstract (English)
We present a methodology to model articulated objects using a sparse set of images with unknown poses. Current methods require dense multi-view observations and ground-truth camera poses. Our approach operates with as few as four views per articulation and no camera supervision. Our central insight is to first solve a robust correspondence and alignment problem between unaligned reconstructions, before part motions can be analyzed. We first reconstruct each articulation independently using recent advances in sparse-view 3D reconstruction, then learn a deformation field that establishes dense correspondences across poses. A progressive disentanglement strategy further separates static from moving parts, enabling robust separation of camera and object motion. Finally, we optimize geometry, appearance, and kinematics jointly with a self-supervised loss that enforces cross-view and cross-pose consistency. Experiments on the standard benchmark and real-world examples demonstrate that our method produces accurate and detailed articulated object representations under significantly weaker input assumptions than existing approaches.
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