Indraprastha Institute of Information Technology, Delhi
We present Holoview, an augmented reality (AR) system designed to support immersive and interactive learning of human anatomy. Holoview enables users to dynamically explore volumetric anatomical data through intuitive hand gestures in a 3D AR environment, allowing inspection of individual
Colorization involves the synthesis of colors while preserving structural content as well as the semantics of the target image. This problem has been well studied for 2D photographs with many state-of-the-art solutions. We explore a new challenge in the field
A good transfer function in volume rendering requires careful consideration of the materials present in a volume. In this work we propose a graph based method that considerably reduces manual effort required in designing a transfer function and provides an
In this work we propose a technique to compute optimal camera poses for image acquisition to be used for drone assisted automated surveys. We use this technique to create highly detailed 3D models of architectural buildings from multi-view reconstruction.
In this work, we address the two main topics of navigation and space identification within the context of augmented reality. Creating navigable digital assets from real-life buildings is a cumbersome task. We present a mostly automated pipeline to create such replicas
In this project, we propose a virtual reality (VR) based immersive walkthrough inside a CT or MRI volume. State-of-the-art rendering algorithms are computationally expensive. This work explores high-quality rendering in real-time using graphics hardware. Various goals of the project
With this project, we propose to build a smart campus infrastructure. A 3D interactive and immersive virtual/mixed reality environment will be designed to support geospatial services including smart navigation and telepresence. Two key aspects of the system are 3D modeling
This research aims at investigating algorithms for 3D surface reconstruction from cross sections. We look at topologically motivated algorithms for robust surface reconstruction. Object reconstruction is a challenging problem at the forefront of computer graphics and vision. A rather less-explored
This work presents a shape aware extension to the Moving Least Squares (MLS) deformation algorithm proposed by Schaefer et al. The classical MLS deformation suffers from quality of deformation for concave objects since the weights are based on the L2
Level set method based segmentation provides an efficient tool for topological and geometrical shape handling, but it is slow due to high computational burden. In this work, we provide a framework for streaming computations on large volumetric images on the