论文标题
使用分段平面模型完成深度
Depth Completion using Piecewise Planar Model
论文作者
论文摘要
深度图可以用一组学习的基础来表示，并且可以在封闭形式的解决方案中有效地解决。但是，这种方法的一个问题是，当颜色边界与深度边界不一致时，它可能会创建工件。实际上，这在自然形象中非常普遍。为了解决这个问题，我们在深度恢复中实施了更严格的模型：零件平面模型。更具体地说，我们将所需的深度图表示为3D平面的集合，重建问题被提出为平面参数的优化。这样的问题可以作为连续的CRF优化问题提出，并且可以通过基于粒子的方法（MP-PBP）\ cite {yamaguchi14}来解决。对Kitti视觉探光数据集的广泛实验评估表明，我们提出的方法具有对错误对象边界的高阻力，并且可以生成有用且视觉上令人愉悦的3D点云。
A depth map can be represented by a set of learned bases and can be efficiently solved in a closed form solution. However, one issue with this method is that it may create artifacts when colour boundaries are inconsistent with depth boundaries. In fact, this is very common in a natural image. To address this issue, we enforce a more strict model in depth recovery: a piece-wise planar model. More specifically, we represent the desired depth map as a collection of 3D planar and the reconstruction problem is formulated as the optimization of planar parameters. Such a problem can be formulated as a continuous CRF optimization problem and can be solved through particle based method (MP-PBP) \cite{Yamaguchi14}. Extensive experimental evaluations on the KITTI visual odometry dataset show that our proposed methods own high resistance to false object boundaries and can generate useful and visually pleasant 3D point clouds.
