no code implementations • ICCV 2023 • Francesco Pittaluga, Bingbing Zhuang
Modern computer vision services often require users to share raw feature descriptors with an untrusted server.
no code implementations • CVPR 2023 • Zhixiang Min, Bingbing Zhuang, Samuel Schulter, Buyu Liu, Enrique Dunn, Manmohan Chandraker
Monocular 3D object localization in driving scenes is a crucial task, but challenging due to its ill-posed nature.
no code implementations • CVPR 2022 • Inkyu Shin, Yi-Hsuan Tsai, Bingbing Zhuang, Samuel Schulter, Buyu Liu, Sparsh Garg, In So Kweon, Kuk-Jin Yoon
In this paper, we propose and explore a new multi-modal extension of test-time adaptation for 3D semantic segmentation.
no code implementations • ICCV 2021 • Donghyun Kim, Yi-Hsuan Tsai, Bingbing Zhuang, Xiang Yu, Stan Sclaroff, Kate Saenko, Manmohan Chandraker
Learning transferable and domain adaptive feature representations from videos is important for video-relevant tasks such as action recognition.
no code implementations • CVPR 2021 • Bingbing Zhuang, Manmohan Chandraker
While we focus on relative pose, we envision that our pipeline is broadly applicable for fusing classical geometry and deep learning.
no code implementations • CVPR 2022 • Buyu Liu, Bingbing Zhuang, Manmohan Chandraker
We propose an end-to-end network that takes a single perspective RGB image of a complex road scene as input, to produce occlusion-reasoned layouts in perspective space as well as a parametric bird's-eye-view (BEV) space.
no code implementations • ECCV 2020 • Bingbing Zhuang, Quoc-Huy Tran
In this paper, we derive a new differential homography that can account for the scanline-varying camera poses in Rolling Shutter (RS) cameras, and demonstrate its application to carry out RS-aware image stitching and rectification at one stroke.
no code implementations • CVPR 2020 • Buyu Liu, Bingbing Zhuang, Samuel Schulter, Pan Ji, Manmohan Chandraker
(2) Introducing the LSTM and FTM modules improves the prediction consistency in videos.
1 code implementation • ECCV 2020 • Lokender Tiwari, Pan Ji, Quoc-Huy Tran, Bingbing Zhuang, Saket Anand, Manmohan Chandraker
Classical monocular Simultaneous Localization And Mapping (SLAM) and the recently emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely disjoint approaches towards building a 3D map of the surrounding environment.
no code implementations • 30 Jul 2019 • Bingbing Zhuang, Quoc-Huy Tran, Pan Ji, Gim Hee Lee, Loong Fah Cheong, Manmohan Chandraker
Self-calibration of camera intrinsics and radial distortion has a long history of research in the computer vision community.
no code implementations • CVPR 2019 • Bingbing Zhuang, Quoc-Huy Tran, Pan Ji, Loong-Fah Cheong, Manmohan Chandraker
In view of the complex RS geometry, we then propose a Convolutional Neural Network (CNN)-based method which learns the underlying geometry (camera motion and scene structure) from just a single RS image and perform RS image correction.
no code implementations • ICCV 2017 • Bingbing Zhuang, Loong-Fah Cheong, Gim Hee Lee
We demonstrate that the dense depth maps recovered from the relative pose of the RS camera can be used in a RS-aware warping for image rectification to recover high-quality Global Shutter (GS) images.
no code implementations • CVPR 2018 • Bingbing Zhuang, Loong-Fah Cheong, Gim Hee Lee
Many existing translation averaging algorithms are either sensitive to disparate camera baselines and have to rely on extensive preprocessing to improve the observed Epipolar Geometry graph, or if they are robust against disparate camera baselines, require complicated optimization to minimize the highly nonlinear angular error objective.