Pradyumna Reddy

I am now a 2nd year PhD at Smart geometry Group, at University College London. My PhD advisor is Prof. Niloy J. Mitra.

Broadly, I am interested in learning structured representations using unsupervised methods. More specifically my work involves using learning based algorithms for analysis of unstructured data like graphs, point clouds, meshes, and vector graphics.

I completed my B.E Hons Computer Science and Engineering from BITS Pilani Goa, did my undergrad-thesis at Laboratory of Mathematics of Imaging, Psychiatry NeuroImaging Laboratory Harvard Medical School, where I worked with Prof. Yogesh Rathi. After that I worked as a Statistical Analyst with the Data and Analytics group at Walmart Labs for couple of years.

Email  /  GitHub  /  Google Scholar  /  LinkedIn

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Research

My reaserch lies at the interesection of computer graphics, machine learning and optimization.

Publications

These include papers accepted to conferences or journels and pre-prints.

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Im2Vec: Synthesizing Vector Graphics without Vector Supervision


Pradyumna Reddy, Michael Gharbi, Michal Lukac, Niloy J. Mitra
CVPR 2021(Oral Presentation), 2021

Vector graphics are widely used to represent fonts, logos, digital artworks, and graphic designs. But, while a vast body of work has focused on generative algorithms for raster images, only a handful of options exists for vector graphics. One can always rasterize the input graphic and resort to image-based generative approaches, but this negates the advantages of the vector representation. The current alternative is to use specialized models that require explicit supervision on the vector graphics representation at training time. This is not ideal because large-scale high quality vector-graphics datasets are difficult to obtain. Furthermore, the vector representation for a given design is not unique, so models that supervise on the vector representation are unnecessarily constrained. Instead, we propose a new neural network that can generate complex vector graphics with varying topologies, and only requires indirect supervision from readily-available raster training images (i.e., with no vector counterparts). To enable this, we use a differentiable rasterization pipeline that renders the generated vector shapes and composites them together onto a raster canvas. We demonstrate our method on a range of datasets, and provide comparison with state-of-the-art SVG-VAE and DeepSVG, both of which require explicit vector graphics supervision. Finally, we also demonstrate our approach on the MNIST dataset, for which no groundtruth vector representation is available. Paper

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Discovering Pattern Structure Using Differentiable Compositing


Pradyumna Reddy, Paul Guerrero, Matt Fisher, Wilmot Li, Niloy J. Mitra
Siggraph Asia 2020, 2020

Patterns, which are collections of elements arranged in regular or near-regular arrangements, are an important graphic art form and widely used due to their elegant simplicity and aesthetic appeal. When a pattern is encoded as a flat image without the underlying structure, manually editing the pattern is tedious and challenging as one has to both preserve the individual element shapes and their original relative arrangements. State-of-the-art deep learning frameworks that operate at the pixel level are unsuitable for manipulating such patterns. Specifically, these methods can easily disturb the shapes of the individual elements or their arrangement, and thus fail to preserve the latent structures of the input patterns. We present a novel differentiable compositing operator using pattern elements and use it to discover structures, in the form of a layered representation of graphical objects, directly from raw pattern images. This operator allows us to adapt current deep learning based image methods to effectively handle patterns. We evaluate our method on a range of patterns and demonstrate superiority in the context of pattern manipulations when compared against state-of-the-art pixel-based pixel- or point-based alternatives. Paper

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SeeThrough: Finding Objects in Heavily Occluded Indoor Scene Images


Moos Hueting, Pradyumna Reddy, Ersin Yumer, Vladimir G. Kim, Nathan Carr, Niloy J. Mitra
3DV 2018(Oral Presentation), 2018

Discovering 3D arrangements of objects from single indoor images is important given its many applications including interior design, content creation, etc. Although heavily researched in the recent years, existing approaches break down under medium or heavy occlusion as the core object detection module starts failing in absence of directly visible cues. Instead, we take into account holistic contextual 3D information, exploiting the fact that objects in indoor scenes co-occur mostly in typical near-regular configurations. First, we use a neural network trained on real indoor annotated images to extract 2D keypoints, and feed them to a 3D candidate object generation stage. Then, we solve a global selection problem among these 3D candidates using pairwise co-occurrence statistics discovered from a large 3D scene database. We iterate the process allowing for candidates with low keypoint response to be incrementally detected based on the location of the already discovered nearby objects. Focusing on chairs, we demonstrate significant performance improvement over combinations of state-of-the-art methods, especially for scenes with moderately to severely occluded objects. Paper

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Joint Multi-Fiber NODDI Parameter Estimation and Tractography Using the Unscented Information Filter


Pradyumna Reddy*, Yogesh Rathi
Frontiers of Neuroscience, 2016

Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging) model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF) to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF). Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters), which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts.

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A Braille-based mobile communication and translation glove for deaf-blind people


Tanay Choudhary*, Saurabh Kulkarni*, Pradyumna Reddy*
ICPC, 2015

Deafblind people are excluded from most forms of communication and information. This paper suggests a novel approach to support the communication and interaction of deaf- blind individuals, thus fostering their independence. It includes a smart glove that translates the Braille alphabet, which is used almost universally by the literate deafblind population, into text and vice versa, and communicates the message via SMS to a remote contact. It enables user to convey simple messages by capacitive touch sensors as input sensors placed on the palmer side of the glove and converted to text by the PC/mobile phone. The wearer can perceive and interpret incoming messages by tactile feedback patterns of mini vibrational motors on the dorsal side of the glove. The successful implementation of real-time two- way translation between English and Braille, and communication of the wearable device with a mobile phone/PC opens up new opportunities of information exchange which were hitherto un-available to deafblind individuals, such as remote communication, as well as parallel one-to many broadcast. The glove also makes communicating with laypersons without knowledge of Braille possible, without the need for trained interpreters.

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Selective Visualization of Anomalies in Fundus Images via Sparse and Low Rank Decomposition


Amol Mahurkar*, Ameya Joshi*, Naren Nallapareddy*, Pradyumna Reddy*, Micha Feigin, Achuta Kadambi, Ramesh Raskar
Siggraph Poster, 2014





Other Projects

These include coursework, side projects and unpublished research work.


Design and source code from Jon Barron's website