Multi-Scale Surface Normal Estimation from Depth Maps

Diclehan Ulucan, Oguzhan Ulucan, Marc Ebner

Abstract: Surface normal vectors are important local descriptors of images, which are utilized in many applications in the field of computer vision and computer graphics. Hence, estimating the surface normals from structured range sensor data is an important step for many image processing pipelines. Thereupon, we present a simple yet effective, learning-free surface normal estimation strategy for both complete and incomplete depth maps. The proposed method takes advantage of scale-space. While the finest scale is used for the initial estimations, the missing surface normals, which cannot be estimated properly are filled from the coarser scales of the pyramid. The same procedure is applied for incomplete depth maps with a slight modification, where we guide the algorithm using the gradient information obtained from the shading image of the scene, which has a geometric relationship with the surface normals. In order to test our method for the incomplete depth maps scenario, we augmented the MIT-