An image of Aditya Wagh

Aditya Wagh

👋 Hi. I’m Aditya, a Computer Vision Engineer & Researcher in the AI4CE Lab headed by Prof. Chen Feng.

I am interested in multimodal perception, localization & mapping algorithms for autonomous systems.

I received my MS from New York University and BEng at BITS Pilani, both in Electrical Engineering, with a specialization in Robotics, AI and Computer Vision.

During my undergrad, I worked with Dr. Sumeet Saurav in the Intelligent Systems Research Group at CEERI, Pilani, a prominent federal research institute.

In my free time, I watch science fiction, cook delicious food and contribute to open-source projects! To get a better insight on my work, have a look at my 📃Resume

Projects

Below is a list of my notable projects. Feel free to have a look at whatever you find interesting!

SuperSLAM: Open Source Framework for Deep Learning based Visual SLAM

SuperSLAM

SuperSLAM: Open Source Framework for Deep Learning based Visual SLAM

Code
Pose estimation pipeline to enable 3D reconstruction using Local Feature Transformer (LoFTR)

Deep Image Matching using Local Feature Transformer

Pose estimation pipeline to enable 3D reconstruction using Local Feature Transformer (LoFTR)

Code
Optimal state estimation using extended & unscented kalman filter implemented using MATLAB

State Estimation of a Drone by Visual-Inertial Sensor Fusion

Optimal state estimation using extended & unscented kalman filter implemented using MATLAB

Code
Benchmarking of LiDAR 3D Object Detection networks (VoxelNet, PointNet++, PointPillars) on KITTI Dataset.

3D Object Detection in LiDAR Point Clouds

Benchmarking of LiDAR 3D Object Detection networks (VoxelNet, PointNet++, PointPillars) on KITTI Dataset.

Code
A visualization in reduced dimension to show how autoencoders learn features in the Fashion MNIST dataset.

Dimensionality Reduction using Convolutional Autoencoders

A visualization in reduced dimension to show how autoencoders learn features in the Fashion MNIST dataset.

Code
Fast, Parallel implementation of the RANSAC algorithm to segment a plane in a point cloud.

Fast Plane Extraction from a 3D Point Cloud

Fast, Parallel implementation of the RANSAC algorithm to segment a plane in a point cloud.

Code
A convolutional neural network for multi-task visual damage assesment.

Post-Earthquake damage assesment using FCNs

A convolutional neural network for multi-task visual damage assesment.

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Relative Pose estimation using OpenCV

Two-View geometry based pose estimation

Relative Pose estimation using OpenCV

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Convolutional Neural Network for detecting power cables.

Power cable detection using Deep Learning

Convolutional Neural Network for detecting power cables.

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A modification in a RNN unit that reduces redundant computation.

Variable Computation in RNNs

A modification in a RNN unit that reduces redundant computation.

Code
A tango inspired dark theme for Visual Studio Code.

Tango+

A tango inspired dark theme for Visual Studio Code.

Code
A simple and elegant Jekyll theme for portfolio pages

Clean Portfolio

A simple and elegant Jekyll theme for portfolio pages

Code