3D indoor reconstruction has been an important research area in the field of computer vision and photogrammetry. While the initial techniques developed for this purpose use sensor devices and multiple images for data acquisition and extracting 3D information and representation of the scene, with the advent of deep learning techniques, there has been good progress in extracting 3D information of an indoor scene reconstruction using a single image. This has potential in minimizing user efforts and cost for data acquisition. The current state-of-the-art method involves two main components, the global depth map and plane instances. After investigating the current state-of-the-art methods, it is observed that there is inconsistency in reconstructed surface boundaries and depth estimation over the curvature and edges of the objects present in the scene, despite having good 3D representation in the surrounding regions. We devise a loss function for optimizing depth estimation during supervision of the neural network by providing geometric awareness to the pixels at local level based on its neighborhood properties defined by spatial compatibility and color similarity. A similar function is used during 3D reconstruction for orientation consistency in the point cloud. Based on the quantitative and qualitative analysis, it is observed that the proposed approach helps in improving the 3D reconstruction from a single image in an indoor environment.

Indoor localisation is a highly relevant topic. It can be used for many applications,
such as indoor navigation. Current indoor localisation approaches all have certain
downsides. In this report, the results of a completely new indoor localisation approach are described. The aim of this approach is to perform indoor localisation on room level based on a fingerprint solution using point clouds of the ceilings. The ceiling is used, because the ceiling does generally not change much and therefore it is easier to keep an up-to-date database. This research considers both the use of Dense Image Matching (DIM) input from pictures or videos made with a mobile phone and Light Detection And Ranging (LiDAR) input.