My master thesis, written at the Autonomous Vision Group of Max Planck Institute for Intelligent Systems under the supervision of Prof. Andreas Geiger, addresses the problem of 3D shape completion of sparse point clouds under weak supervision. Specifically, based on a learned shape prior it is possible to learn 3D shape completion without access to ground truth shapes, as shown on KITTI. This article briefly introduces the problem and the main contributions and offers the thesis as download.
Part of my master thesis at the Max Planck Institute for Intelligent Systems was an initial proposal — outlining the general idea and the current state-of-the-art. Specifically, I worked on learning 3D shape completion on KITTI using 3D bounding boxes only. In this article, I want to present this proposal.
My bachelor thesis, written at the Computer Vision Group at RWTH Aachen University, discusses superpixel segmentation utilizing depth information. Based on our own implementation of SEEDS , we examine the influence of depth information on the performance and compare several variants to other state-of-the-art approaches to superpixel segmentation.
Due to my bachelor thesis at RWTH Aachen University I am currently busy learning everything about superpixel segmentation — the oversegmentation of an image into groups of pixels using low-level features. In this article I want to give a short introduction by presenting my bachelor thesis proposal.