Robust Depth-based Planar Segmentation Algorithm based on Gradient of Depth feature

Abstract

In this work, a new algorithm for segmenting planar regions from depth images is proposed. Despite the numerous algorithms that extract planar regions from depth images, developing a new algorithm was motivated by the results of other State-of-Art algorithms which are tailored to segment depth images acquired by laser scanners or structure light cameras, but failed to segment depth images generated from stereo vision of normal textured objects which are found a lot in many indoor scenarios. Unlike other State-of-Art methods which are based mainly on the local surface normal feature for the segmentation process; the proposed planar segmentation algorithm is based on a novel feature to be called Gradient of Depth feature (DoG). The proposed DoG feature is parameter-free, easy to compute in terms of complexity and faster to compute compared to the local surface normal (i.e. the GoD feature is computed in the 2D image space vs. the surface normal which is computed in the 3D point cloud). The proposed feature is implemented into a robust algorithm that utilizes the 1D dimension feature space of the  GoD feature which in turn increases the robustness of the algorithm to parameters change. The proposed algorithm can be used on different scenes acquired by different cameras. In terms of segmentation accuracy, it does not only meet (or even pass in some cases) the performance of other State-of-Art algorithms, but it is able to segment robustly planar regions from disparity images on which other algorithms have failed. Additionally, the GoD<em>-</em>based algorithm segments planar regions from non-planar objects where classical planar segmentation algorithms fail to segment.