  We propose a novel and efficient representation for single-view depth
estimation using Convolutional Neural Networks (CNNs). Point-cloud is generally
used for CNN-based 3D scene reconstruction; however it has some drawbacks: (1)
it is redundant as a representation for planar surfaces, and (2) no spatial
relationships between points are available (e.g, texture and surface). As a
more efficient representation, we introduce a triangular-patch-cloud, which
represents the surface of the 3D structure using a set of triangular patches,
and propose a CNN framework for its 3D structure estimation. In our framework,
we create it by separating all the faces in a 2D mesh, which are determined
adaptively from the input image, and estimate depths and normals of all the
faces. Using a common RGBD-dataset, we show that our representation has a
better or comparable performance than the existing point-cloud-based methods,
although it has much less parameters.
