Page Not Found
Page not found. Your pixels are in another canvas.
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
Page not in menu
This is a page not in th emain menu
Posts
PhySG in equations
Published:
The rendering equation for PhySG, along a view-direction $o$ from camera, is defined as follows: \(R(o) = \int_{S^2} L(i) \rho_o(i) max(0, i.n) di\)
Rendering using Spherical Gaussians
Published:
The rendering equation, along a view-direction $o$ from camera, is defined as follows:
Spherical Gaussians
Published:
A spherical Gaussian is a function defined on the surface of a sphere. It is defined as follows:
Deep Learning based 3D Human Body Reconstruction
Published:
This post mainly covers following works in brief:
Why do I need efficiency when I have a great GPU?
Published:
I got the idea of this blog post when a friend of mine shared an answer on Quora. In this answer, Miguel Oliveira explains how the Machine Learning algorithm performed effectively, that is, took less time on increasing training batch size just because of changing to a more efficient data structure for the problem. This concept is usually overlooked by many people in the industries, making them wondering if they should actually care about efficient algrithms.
Welcome to my Website!
Published:
Welcome to my Website! I am really glad that you are here, and hope that you can make the most out of it. I will be updating my profile and information and maintain it, along with writing about latest research in my field. I hope this keeps me up-to-date with latest works, and simultaneously, can be helpful for others.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
Transfer Learning by Finetuning Pretrained CNNs Entirely with Synthetic Images
Published in Proceeding of the National Conference on Computer Vision, Pattern Recognition, Image Processing, and Graphics NCVPRIPG 2017 in CCIS, vol 841 Series of Springer, Singapore, 2018
This paper discusses our approach of transferring the learned object detection features trained entirely on Synthetic Images and tested on Real Images, which show significant performance improvements, along with easily generatable huge data for Deep Neural Networks.
Rajpura P. et al. (2018) Transfer Learning by Finetuning Pretrained CNNs Entirely with Synthetic Images. In: Rameshan R., Arora C., Dutta Roy S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore
Broad Neural Network for Change Detection in Aerial Images
Published in Proceedings of the International Conference on Emerging Technologies in Graphics IEMGraph 2018 in AICS, vol 937 Series of Springer, Singapore, 2019
This paper discusses about an improvement in Broad Neural Network’s architecture, and its performace in highly imbalanced data formed by introducing changes in satellite or aerial images of landscapes.
Shrivastava S., Aggarwal A., Chattopadhyay P. (2020) Broad Neural Network for Change Detection in Aerial Images. In: Mandal J., Bhattacharya D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore
EPD-Net: A GAN-based Architecture for Face De-identification from Images
Published in 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), 2020
This paper introduces a GAN-based architecture to de-identify the faces from public dataset for privacy protection, without the loss of other non-biometric information.
Aggarwal, A., Rathore, R., Chattopadhyay, P., & Wang, L. (2020, September). EPD-Net: A GAN-based Architecture for Face De-identification from Images. In 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1-7). IEEE.
Layered-Garment Net: Generating Multiple Implicit Garment Layers from a Single Image
Published in 16th Asian Conference on Computer Vision (ACCV2022), 2022
This paper reconstructs multiple implicit layers of intersection-free garments on an implicit human body
Aggarwal, A., Wang, J., Hogue, S., Ni, S., Budagavi, M., & Guo, X. (2022). Layered-Garment Net: Generating Multiple Implicit Garment Layers from a Single Image. In Proceedings of the Asian Conference on Computer Vision (pp. 3000-3017).
TexHOI: Reconstructing Textures of 3D Unknown Objects in Monocular Hand-Object Interaction Scenes
Published in IEEE TVCG (CVM 2025), 2024
This paper reconstructs 3D objects albedo with unknown illumination, in hand-object interaction scenes.
Aggarwal, A., Wang, N. & Guo, X. (2024). TexHOI: Reconstructing Textures of 3D Unknown Objects in Monocular Hand-Object Interaction Scenes.
talks
Transfer Learning by Finetuning Pretrained CNNs Entirely with Synthetic images
Published:
A poster presentation explaining my paper.
Deep Learning based 3D Human body reconstruction
Published:
A slide explaining mainly two approaches - Surface based and Volume based, using the underlying SMPL model for reconstructing human body using deep learning approached.
PiFUHD
Published:
A slide explaining a multi-level image/pixel aligned implicit function based human body reconstruction in high-resolution.
SoftRas
Published:
A slide explaining a differential renderer for 3D based image reasoning, including reasoning of occluded regions.
PixelNeRF
Published:
A slide explaining a image-conditioned or pixel-aligned Neural Network based Radiance Field. Radiance Fields are used to obtain resultant color and opacity of any point in 3D space, with respect to view direction.
NeRO
Published:
A slide explaining a Neural Network based Radiance Field handling reflective objects.
teaching
Introduction to Computer Programming
Undergraduate course, IIT (BHU) Varanasi, 2018
Intoroduction to the basics of Computer Programming, functioning of different programming languages, introduction to C Programming Language and Problem Solving.
Artificial Intelligence
Teaching Assistantship, UT Dallas, 2019
Introduction to Logic, Intelligence and Machine Learning algorithms to solve classification, regression, data retrieval problems. LISP and AIMA.
Database Systems
Teaching Assistantship, UT Dallas, 2020
Entity, Relationship Diagram, Relational mapping, SQL, Relational Algebra, Normalization, Query Optimization, Transaction Processing, Fault recovery
Computer Graphics
Teaching Assistantship, UT Dallas, 2021
Course based on “Computer Graphics for Java Programmers (3rd Edition)” by Leen Ammeraal and Kang Zhang
Computer Animation and Gaming
Teaching Assistantship, UT Dallas, 2021
OpenGL and GLSL, Vectors, Matrix, Cubic Curves, Interpolating Values, Quaternion, Interpolating Orientation, Forward Kinematics, Skin, Inverse Kinematics.
Design and Analysis of Algorithm
Teaching Assistantship, UT Dallas, 2023
Algorithmic analysis and design. Algorithmic paradigms: divide and conquer, dynamic programming, greedy algorithms, randomized algorithms, approximation algorithms. Algorithmic techniques: backtracking, branch and bound, graph algorithms, string algorithms, computational geometry. Algorithmic complexity: time and space complexity, worst-case and average-case analysis, asymptotic notation, master theorem. NP-completeness and P vs NP.