Akash Bapat | Computer Vision

Hi, my name is Akash Bapat. I am a Research Scientist and I work in the field of Augmented and Virtual Reality at Meta Inc. Previously, I completed my Ph.D from the computer science department of UNC Chapel Hill and was advised by Jan-Michael Frahm. My research interests can be traced to my IITGN days where I worked with Prof. Raman.

Recent News

• Our paper on stereo system for smart glasses was accepted at CVPR 20223, see arxiv

Papers

	A Practical Stereo Depth System for Smart Glasses Jialiang Wang, Daniel Scharstein, Akash Bapat, and many other people To appear in CVPR, 2023 bibtex End to end stereo ML system for smart glasses.
	Thesis: Towards High-Frequency Tracking and Fast Edge-Aware Optimization Akash Bapat UNC Chapel Hill Doctoral Thesis. bibtex My thesis covering my work on rolling shutter tracking and edge aware optimization.
	Boundary-aware 3D Building Reconstruction from a Single Overhead Image Jisan Mahmud, True Price, Akash Bapat, and Jan-Michael Frahm Appeared in CVPR, 2020 bibtex Predicts building boundaries, semantics, signed distance function (BPSH) and normalized DSMs using a single overhead or satellite image using a multi-task deep learning framework.
	Mapped Convolutions Marc Eder, True Price, Thanh Vu, Akash Bapat, and Jan-Michael Frahm bibtex Decouples weighted sum and sampling in a convolution operation to enable processing 360 imagery which show high levels of distortion.
	The Domain Transform Solver Akash Bapat and Jan-Michael Frahm CVPR, 2019 arxiv / code / bibtex Fast edge-aware optimization can be done by using approximate 1-D filtering techniques.
	Rolling Shutter and Radial Distortion are Features for High Frame Rate Multi-camera Tracking Akash Bapat, True Price and Jan-Michael Frahm CVPR, 2018 poster / supp / bibtex Radial distortion induces multiple virtual rolling shutter cameras. Using these virtual cameras, we can better constrain the head-pose motion and still track at a high-frequency.
	Towards Kilo-Hertz 6-DoF Visual Tracking Using an Egocentric Cluster of Rolling Shutter Cameras Akash Bapat, Enrique Dunn and Jan-Michael Frahm ISMAR/TVCG, 2016, Best Paper Award talk / bibtex Rolling shutter exposure provides us with a high fequency of row-image samples. If we can estimate a pose per-row, then we have a high-frequency tracker.