Scene Segmentation Driven by Deep Learning and Surface Fitting

This paper proposes a joint color and depth segmentation scheme exploiting together geometrical clues and a learning stage. The approach starts from an initial over-segmentation based on spectral clustering. The input data is also fed to a Convolutional Neural Network (CNN) thus producing a per-pixel descriptor vector for each scene sample. An iterative merging procedure is then used to recombine the segments into the regions corresponding to the various objects and surfaces. The proposed algorithm starts by considering all the adjacent segments and computing a similarity metric according to the CNN features. The couples of segments with higher similarity are considered for merging. Finally the algorithm uses a NURBS surface fitting scheme on the segments in order to understand if the selected couples correspond to a single surface. The comparison with state-of-the-art methods shows how the proposed method provides an accurate and reliable scene segmentation

Towards automatic reconstruction of indoor scenes from incomplete point clouds: door and window detection and regularization

In the last years, point clouds have become the main source of information for building modelling. Although a considerable amount of methodologies addressing the automated generation of 3D models from point clouds have been developed, indoor modelling is still a challenging task due to complex building layouts and the high presence of severe clutters and occlusions. Most of methodologies are highly dependent on data quality, often producing irregular and non-consistent models. Although manmade environments generally exhibit some regularities, they are not commonly considered. This paper presents an optimization-based approach for detecting regularities (i.e., same shape, same alignment and same spacing) in building indoor features. The methodology starts from the detection of openings based on a voxel-based visibility analysis to distinguish ‘occluded’ from ‘empty’ regions in wall surfaces. The extraction of regular patterns in windows is addressed from studying the point cloud from an outdoor perspective. The layout is regularized by minimizing deformations while respecting the detected constraints. The methodology applies for elements placed in the same planeXunta de Galicia | Ref. ED481B 2016/079-

TOWARDS AUTOMATIC RECONSTRUCTION OF INDOOR SCENES FROM INCOMPLETE POINT CLOUDS: DOOR AND WINDOW DETECTION AND REGULARIZATION

In the last years, point clouds have become the main source of information for building modelling. Although a considerable amount of methodologies addressing the automated generation of 3D models from point clouds have been developed, indoor modelling is still a challenging task due to complex building layouts and the high presence of severe clutters and occlusions. Most of methodologies are highly dependent on data quality, often producing irregular and non-consistent models. Although manmade environments generally exhibit some regularities, they are not commonly considered. This paper presents an optimization-based approach for detecting regularities (i.e., same shape, same alignment and same spacing) in building indoor features. The methodology starts from the detection of openings based on a voxel-based visibility analysis to distinguish ‘occluded’ from ‘empty’ regions in wall surfaces. The extraction of regular patterns in windows is addressed from studying the point cloud from an outdoor perspective. The layout is regularized by minimizing deformations while respecting the detected constraints. The methodology applies for elements placed in the same plane

Acquisition and Processing of ToF and Stereo data

Providing a computer the capability to estimate the three-dimensional geometry of a scene is a fundamental problem in computer vision. A classical systems that has been adopted for solving this problem is the so-called stereo vision system (stereo system). Such a system is constituted by a couple of cameras and it exploits the principle of triangulation in order to provide an estimate of the framed scene. In the last ten years, new devices based on the time-of-flight principle have been proposed in order to solve the same problem, i.e., matricial Time-of-Flight range cameras (ToF cameras). This thesis focuses on the analysis of the two systems (ToF and stereo cam- eras) from a theoretical and an experimental point of view. ToF cameras are introduced in Chapter 2 and stereo systems in Chapter 3. In particular, for the case of the ToF cameras, a new formal model that describes the acquisition process is derived and presented. In order to understand strengths and weaknesses of such different systems, a comparison methodology is introduced and explained in Chapter 4. From the analysis of ToF cameras and stereo systems it is possible to understand the complementarity of the two systems and it is intuitive to figure that a synergic fusion of their data might provide an improvement in the quality of the measurements preformed by the two devices. In Chapter 5 a method for fusing ToF and stereo data based on a probability approach is presented. In Chapter 6 a method that exploits color and three-dimensional geometry information for solving the classical problem of scene segmentation is explaine