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Found 13 papers, 1 papers with code
LDP-Feat: Image Features with Local Differential Privacy
Modern computer vision services often require users to share raw feature descriptors with an untrusted server.
NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization
Monocular 3D object localization in driving scenes is a crucial task, but challenging due to its ill-posed nature.
MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation
In this paper, we propose and explore a new multi-modal extension of test-time adaptation for 3D semantic segmentation.
Learning Cross-modal Contrastive Features for Video Domain Adaptation
Learning transferable and domain adaptive feature representations from videos is important for video-relevant tasks such as action recognition.
Fusing the Old with the New: Learning Relative Camera Pose with Geometry-Guided Uncertainty
While we focus on relative pose, we envision that our pipeline is broadly applicable for fusing classical geometry and deep learning.
Weakly But Deeply Supervised Occlusion-Reasoned Parametric Road Layouts
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.
Image Stitching and Rectification for Hand-Held Cameras
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.
Understanding Road Layout from Videos as a Whole
(2) Introducing the LSTM and FTM modules improves the prediction consistency in videos.
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction
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.
Degeneracy in Self-Calibration Revisited and a Deep Learning Solution for Uncalibrated SLAM
Self-calibration of camera intrinsics and radial distortion has a long history of research in the computer vision community.
Learning Structure-And-Motion-Aware Rolling Shutter Correction
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.
Rolling-Shutter-Aware Differential SfM and Image Rectification
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.
Baseline Desensitizing In Translation Averaging
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.
