Reconstructing 3D geometry and appearance from a sparse set of fixed cameras is a foundational task with broad applications, yet it remains fundamentally constrained by the limited viewpoints. We show that this bound can be broken by exploiting opportunistic object motion: as a person manipulates an object~(e.g., moving a chair or lifting a mug), the static cameras effectively ``orbit'' the object in its local coordinate frame, providing additional virtual viewpoints. Harnessing this object motion, however, poses two challenges: the tight coupling of object pose and geometry estimation and the complex appearance variations of a moving object under static illumination. We address these by formulating a joint pose and shape optimization using 2D Gaussian splatting with alternating minimization of 6DoF trajectories and primitive parameters, and by introducing a novel appearance model that factorizes diffuse and specular components with reflected directional probing within the spherical harmonics space. Extensive experiments on synthetic and real-world datasets with extremely sparse viewpoints demonstrate that our method recovers significantly more accurate geometry and appearance than state-of-the-art baselines.

翻译：从稀疏固定相机集合中重建三维几何与外观是一项基础性任务，具有广泛的应用场景，但始终受限于有限的视角。我们证明，通过利用偶然的物体运动可以突破这一限制：当人操作物体时（例如移动椅子或举起杯子），静态相机实际上会在物体的局部坐标系中“环绕”物体，从而提供额外的虚拟视角。然而，利用这种物体运动面临两大挑战：物体姿态与几何估计的紧密耦合，以及静态光照下运动物体的复杂外观变化。我们通过以下方法解决这些问题：采用二维高斯泼溅法，结合对六自由度轨迹与图元参数的交替最小化，构建联合姿态与形状优化；并引入一种新型外观模型，该模型在球谐函数空间内通过反射方向探测来实现漫反射与镜面反射分量的分解。在视角极为稀疏的合成与真实数据集上的大量实验表明，我们的方法相比最先进的基线模型，能够恢复出显著更精确的几何与外观。