We propose a method for converting a single RGB-D input image into a 3D photo - a multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view. We use a Layered Depth Image with explicit pixel connectivity as underlying representation, and present a learning-based inpainting model that synthesizes new local color-and-depth content into the occluded region in a spatial context-aware manner. The resulting 3D photos can be efficiently rendered with motion parallax using standard graphics engines. We validate the effectiveness of our method on a wide range of challenging everyday scenes and show fewer artifacts compared with the state of the arts.
翻译:我们提出了一个将单一 RGB-D 输入图像转换为 3D 照片的方法 — — 包含原始视图中隐蔽的区域的幻影颜色和深度结构的新观点合成的多层代表。 我们使用一个具有清晰像素连接的多层深度图像作为基本表达方式,并展示一个基于学习的绘图模型,以空间环境意识的方式将新的本地色彩和深度内容综合到隐蔽区域。 由此产生的3D 照片可以通过使用标准图形引擎的移动抛光器有效制作。 我们验证了我们的方法在众多挑战性的日常场景上的有效性,并展示了与艺术状态相比较少的艺术品。