论文标题
阴影在3D对象上亮起
Shadows Shed Light on 3D Objects
论文作者
论文摘要
3D重建是计算机视觉中的一个基本问题，当重建对象被部分或完全遮挡时，任务尤其具有挑战性。我们介绍了一种使用未观察到的对象施放的阴影，以推断遮挡后面可能的3D卷。我们创建了一个可区分的图像形成模型，使我们能够共同推断物体的3D形状，其姿势和光源的位置。由于该方法是端到端可区分的，因此我们能够整合对象几何学的学先先验，以生成不同对象类别的现实3D形状。实验和可视化表明该方法能够生成与阴影观察一致的多种可能的解决方案。即使光源和物体姿势的位置都是未知的，我们的方法也起作用。我们的方法对现实世界的图像也很强大，在现实世界中，地面真实的阴影面罩未知。
3D reconstruction is a fundamental problem in computer vision, and the task is especially challenging when the object to reconstruct is partially or fully occluded. We introduce a method that uses the shadows cast by an unobserved object in order to infer the possible 3D volumes behind the occlusion. We create a differentiable image formation model that allows us to jointly infer the 3D shape of an object, its pose, and the position of a light source. Since the approach is end-to-end differentiable, we are able to integrate learned priors of object geometry in order to generate realistic 3D shapes of different object categories. Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow. Our approach works even when the position of the light source and object pose are both unknown. Our approach is also robust to real-world images where ground-truth shadow mask is unknown.
