Ananya Ipsita

Ananya Ipsita

Ananya Ipsita is a Master's student in the School of Mechanical Engineering at Purdue University since Fall 2018. She received her Bachelor's degree in Electronics and Communication Engineering from National Institute of Technology, Rourkela, India. Prior to joining Purdue, she worked as a software engineer in SAP Labs, India where she designed and developed analytical business solutions. Her research interest includes computer vision, robotic systems, Augmented Reality (AR) and Human-Computer Interaction (HCI).
VRFromX: From Scanned Reality to Interactive Virtual Experience with Human-in-the-Loop

VRFromX: From Scanned Reality to Interactive Virtual Experience with Human-in-the-Loop

Ananya Ipsita, Hao Li, Runlin Duan, Yuanzhi Cao, Subramanian Chidambaram, Min Liu, Karthik Ramani
In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

There is an increasing trend of Virtual-Reality (VR) applications found in education, entertainment, and industry. Many of them utilize real world tools, environments, and interactions as bases for creation. However, creating such applications is tedious, fragmented, and involves expertise in authoring VR using programming and 3D-modelling softwares. This hinders VR adoption by decoupling subject matter experts from the actual process of authoring while increasing cost and time. We present VRFromX, an in-situ Do-It-Yourself (DIY) platform for content creation in VR that allows users to...

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Deep Learning 3D Shapes Using ALT-AZ Anisotropic 2-Sphere Convolution

Deep Learning 3D Shapes Using ALT-AZ Anisotropic 2-Sphere Convolution

Liu, Min, Fupin Yao, Chiho Choi, Sinha Ayan, and Karthik Ramani
In proceedings of Seventh International Conference on Learning Representations (ICLR), New Orleans, May 6-9, 2019

The ground-breaking performance obtained by deep convolutional neural networks (CNNs) for image processing tasks is inspiring research efforts attempting to extend it for 3D geometric tasks. One of the main challenge in applying CNNs to 3D shape analysis is how to define a natural convolution operator on noneuclidean surfaces. In this paper, we present a method for applying deep learning to 3D surfaces using their spherical descriptors and alt-az anisotropic convolution on 2-sphere. A cascade set of geodesic disk filters rotate on the 2-sphere and collect spherical patterns and so to extract...

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WireFab: Mix-Dimension Modeling and Fabrication for 3D Mesh Models

WireFab: Mix-Dimension Modeling and Fabrication for 3D Mesh Models

Min Liu, Yunbo Zhang, Jing Bai, Yuanzhi Cao, Jeffrey Alperovich, Karthik Ramani
In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2017: 965-976), Denver, CO, May 6-11, 2017 (Acceptance Rate: 25%)

We propose WireFab, a rapid modeling and prototyping system that uses bent metal wires as the structure framework. WireFab approximates both the skeletal articulation and the skin appearance of the corresponding virtual skin meshes, and it allows users to personalize the designs by (1) specifying joint positions and part segmentations, (2) defining joint types and motion ranges to build a wire-based skeletal model, and (3) abstracting the segmented meshes into mixed-dimensional appearance patterns or attachments. The WireFab is designed to allow the user to choose how to best preserve the...

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