Previous posters

Synthesis of Depth images from RGB images

Practical Project, Chair of Computer Graphics and Visualization, TUM, Munich

Depth images are a rich source information about their subjects and have been found to be particularly useful for tasks such as 3D Reconstruction. This work aims to learn a supervised pixel to pixel mapping from an RGB image to its corresponding depth image. The architecture in this work is based on the paper by Li, Jun, Reinhard Klein, and Angela Yao. “Learning fine-scaled depth maps from single RGB images.” arXiv preprint (2016)

For more details, see the poster below.

Importance Weighted Autoencoders

Practical Project, Chair of Robotics and Embedded Systems, TUM, Munich

This work re-implements Importance Weighted Autoencoders and Variational Autoencoders. The results and comparisons between these two models are laid out in a poster below.

Iterative Closest Point Analysis

Course Project, Chair of Computer Vision, TUM, Munich

Iterative closest Point (ICP) is an algorithm employed to minimize the difference between two point clouds given an initial estimate of the relative pose. It is often used to reconstruct 2D or 3D surfaces from different scans, to localize robots and achieve optimal path planning and to register medical scans. ICP has several steps and each step may be implemented in various ways which give rise to a multitude of ICP variants. This project implements and analyzes several variants of ICP, comparing them on the basis of execution speed and quality of the result.

IAF_Dynamics

Practical Project, Chair of Robotics and Embedded Systems, TUM, Munich

This work uses Inverse Autoregressive Flows to model each time-step in a sequential data-point and then compares this architecture with the (then) state of the art on the open-ai gym pendulum dataset to show that the current method is only slightly worse, but samples lesser number of times.