Sparse Point Cloud Densification by Combining Multiple Segmentation Methods

TL;DR: This paper presents a novel method for dense 3D reconstruction of man-made environments that clearly outperforms state-of-the-art dense3D reconstruction pipelines and surface layout estimation approaches.

Abstract: This paper presents a novel method for dense 3D reconstruction of man-made environments. Such environments suffer from texture less and non-Lambertian surfaces, where conventional, feature-Based 3D reconstruction pipelines fail to obtain good feature matches. To compensate this lack of feature matches, we exploit the semantic information available in 2D images to estimate both a corresponding 3D position and a 3D surface normal for each pixel. A semantic classifier is therefore applied on a single segmented image in order to get a likelihood for a segment providing one of the surface normals within a discrete set of them. To improve the accuracy of this labeling step, we exploit multiple segmentation methods. The global best surface normal configuration over all pixels of an image is then obtained by using a Markov Random Field. In the last step, the 3D model of a single 2D input image is reconstructed by combining the semantic surface normal estimation with the sparse point cloud coming from feature Based matching. It is shown experimentally, that our proposed method clearly outperforms state-of-the-art dense 3D reconstruction pipelines and surface layout estimation approaches.