no code implementations • 26 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.
no code implementations • 17 Apr 2023 • Pooya Fayyazsanavi, Zhiqiang Wan, Will Hutchcroft, Ivaylo Boyadzhiev, Yuguang Li, Jana Kosecka, Sing Bing Kang
While the existing deep learning-based room layout estimation techniques demonstrate good overall accuracy, they are less effective for distant floor-wall boundary.
1 code implementation • European Conference on Computer Vision 2022 • John Lambert, Yuguang Li, Ivaylo Boyadzhiev, Lambert Wixson, Manjunath Narayana, Will Hutchcroft, James Hays, Frank Dellaert, Sing Bing Kang
We propose a new system for automatic 2D floorplan reconstruction that is enabled by SALVe, our novel pairwise learned alignment verifier.
no code implementations • European Conference on Computer Vision 2022 • Will Hutchcroft, Yuguang Li, Ivaylo Boyadzhiev, Zhiqiang Wan, HaiYan Wang, Sing Bing Kang
We present CoVisPose, a new end-to-end supervised learning method for relative camera pose estimation in wide baseline 360 indoor panoramas.
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.
1 code implementation • CVPR 2022 • HaiYan Wang, Will Hutchcroft, Yuguang Li, Zhiqiang Wan, Ivaylo Boyadzhiev, YingLi Tian, Sing Bing Kang
In this paper, we propose a new deep learning-based method for estimating room layout given a pair of 360 panoramas.
1 code implementation • CVPR 2021 • Steve Cruz, Will Hutchcroft, Yuguang Li, Naji Khosravan, Ivaylo Boyadzhiev, Sing Bing Kang
We present Zillow Indoor Dataset (ZInD): A large indoor dataset with 71, 474 panoramas from 1, 524 real unfurnished homes.
no code implementations • 5 Dec 2016 • Satoshi Ikehata, Ivaylo Boyadzhiev, Qi Shan, Yasutaka Furukawa
This paper addresses the problem of Structure from Motion (SfM) for indoor panoramic image streams, extremely challenging even for the state-of-the-art due to the lack of textures and minimal parallax.