Search Results for author: Manjunath Narayana

Found 6 papers, 1 papers with code

Graph-CoVis: GNN-based Multi-view Panorama Global Pose Estimation

no code implementations26 Apr 2023 Negar Nejatishahidin, Will Hutchcroft, Manjunath Narayana, Ivaylo Boyadzhiev, Yuguang Li, Naji Khosravan, Jana Kosecka, Sing Bing Kang

In this paper, we address the problem of wide-baseline camera pose estimation from a group of 360$^\circ$ panoramas under upright-camera assumption.

Pose Estimation

LASER: LAtent SpacE Rendering for 2D Visual Localization

1 code implementation CVPR 2022 Zhixiang Min, Naji Khosravan, Zachary Bessinger, Manjunath Narayana, Sing Bing Kang, Enrique Dunn, Ivaylo Boyadzhiev

LASER introduces the concept of latent space rendering, where 2D pose hypotheses on the floor map are directly rendered into a geometrically-structured latent space by aggregating viewing ray features.

Indoor Localization Metric Learning +1

Lifelong update of semantic maps in dynamic environments

no code implementations17 Oct 2020 Manjunath Narayana, Andreas Kolling, Lucio Nardelli, Phil Fong

First, as a robot senses new changes and alters its raw map in successive runs, the semantics must be updated appropriately.

Background Modeling Using Adaptive Pixelwise Kernel Variances in a Hybrid Feature Space

no code implementations5 Nov 2015 Manjunath Narayana, Allen Hanson, Erik Learned-Miller

In one, there has been increasing sophistication of probabilistic models, from mixtures of Gaussians at each pixel [7], to kernel density estimates at each pixel [1], and more recently to joint domainrange density estimates that incorporate spatial information [6].

Coherent Motion Segmentation in Moving Camera Videos using Optical Flow Orientations

no code implementations5 Nov 2015 Manjunath Narayana, Allen Hanson, Erik Learned-Miller

Our goal is to develop a segmentation algorithm that clusters pixels that have similar real-world motion irrespective of their depth in the scene.

Motion Segmentation Optical Flow Estimation +1

Background subtraction - separating the modeling and the inference

no code implementations5 Nov 2015 Manjunath Narayana, Allen Hanson, Erik Learned-Miller

In particular, it is essential to have a background likelihood, a foreground likelihood, and a prior at each pixel.

Cannot find the paper you are looking for? You can Submit a new open access paper.