3D Shape Analysis Reconstruction and Classification

Abstract

The reconstruction and analysis of 3D objects by computational systems has been an intensive and long-lasting research problem in the graphics and computer vision scientific communities. Traditional acquisition systems are largely restricted to studio environment setup which requires multiple synchronized and calibrated cameras. With the advent of active depth sensors like time-of-flight sensors, structured lighting sensors made 3D acquisition feasible. This advancement of technology has paved way to many research problems like 3D object localization, recognition, classification, reconstruction which demand innovating sophisticated/elegant solutions to match their ever growing applications. 3D human body reconstruction, in particular, has wider applications like virtual mirror, gait analysis, etc. Lately, with the advent of deep learning, 3D reconstruction from monocular images garnered significant interest among the research community as it can be applied to in-the-wild settings. newline newlineInitially we started exploration of classification of 3D rigid objects due to availabilty of ShapeNet datasets. In this thesis, we propose an efficient characterization of 3D rigid objects which take local geometry features into consideration while constructing global features in the deep learning setup. We introduce learnable B-Spline surfaces in order to sense complex geometrical structures (large curvature variations). The locations of these surfaces are initialized over the voxel space and are learned during training phase leading to efficient classification performance. Later on, we primarily focus on rather challenging problem of non-rigid 3D human body reconstruction from monocular images. In this context, this thesis presents three principle approaches to address 3D reconstruction problem. Firstly, we propose a disentangled solution where we recover shape and texture of the 3D shape predicted using two different networks. We recover the volumetric shape of non-rigid human body shapes given a single view RGB image followed