21 skills found
nianticlabs / Mickey[CVPR 2024 - Oral] Matching 2D Images in 3D: Metric Relative Pose from Metric Correspondences
Zumbalamambo / 2D 3D Pose TrackingMonocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences
Liumouliu / Deep Blind PnPLearning 2D–3D Correspondences To Solve The Blind Perspective-n-Point Problem
hkust-vgd / Lcd[AAAI'20] LCD: Learned Cross-Domain Descriptors for 2D-3D Matching
TruongKhang / DeViLoc[CVPR2024 Oral] Learning to Produce Semi-dense Correspondences for Visual Localization
SergioRAgostinho / CvxpnplA Perspective-n-Points-and-Lines method.
WangYuLin-SEU / HCCEPose[ICCV'25 Highlight] HccePose (BF): Predicting Front & Back Surfaces to Construct Ultra-Dense 2D-3D Correspondences for Pose Estimation
agarwa65 / Lidar Camera CalibrationLidar Camera Callibration in ROS
SergioRAgostinho / Zhou Accv 2018A Python 3 implementation of "A Stable Algebraic Camera Pose Estimation for Minimal Configurations of 2D/3D Point and Line Correspondences." by Zhou et al. ACCV 2018
fdyuandong / 2D 3D Point Set Registration Based On Global Rotation Search# 2D-3D Point Set Registration Based on Global Rotation Search # Copyright (C) 2018 Yinlong Liu@outlook.com # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # Any publications resulting from the use of this code should cite the # following paper: # Yinlong Liu, Yuan Dong, Zhijian Song and Manning Wang, "2D 3D Point Set Registration Based on Global Rotation_Search", IEEE Transactions on Image Processing (TIP) # # #==================Note================= # #1. First step->open demo_rot.m. It is a demo of Rotation Search in SO(3) for 2D-3D point set registration. # #2. The input data is set to a real circumstance that 3D point set is far away for projection plane, and in front of camera. Our method also can be applied to unusual condition that camera is surrounded by 3D point set, only if you make some fix. # #3. RotaionSearch.m is the kernel of algorithm. You can easily extend it to SE(3) searching by grid-search, while tuning parameters depends on your tasks. # #4. Fast and Global 2D-3D point set registration without correspondence is still an open problem and need further explorations. I am very happy that if you could benefit from our code. # # # Author: Yinlong Liu # Date: 20181218 # Revision: 1.0
levenberg / 2D 3D Pose TrackingMonocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences
imbinwang / PosestThe 6DoF pose solver given a set of 2D-3D point correspondences.
CDInstitute / CompoNETCompoNET: geometric deep learning approach in architecture. From a single-image generates a building with all its components
bpfel / VisualOdometry MatlabThe goal of this project is to implement a simple, monocular, visual odometry (VO) pipeline with the most essential features: initialization of 3D landmarks, keypoint tracking between two frames, pose estimation using established 2D / 3D correspondences, and triangulation of new land-marks. In addition, camera calibration, self-generated datasets and sliding window bundle adjustment have been implemented.
qq456cvb / SemanticTransferCode repo for the paper "Semantic Correspondence via 2D-3D-2D Cycle"
Yucao42 / NPnLA non-linear solution to perspective n lines problem, i. e. pose estimation from 3D-2D lines' correspondences.
CenekAlbl / Ar From ColmapObtaining 3D - 2D correspondences from COLMAP reconstruction to test absolute pose algorithms
SubMishMar / MovoMonocular Visual Odometry using 3D-2D correspondences
Madhan-Sureshbabu / Camera Pose EstimationImplementation of relative pose estimation with 2D-2D, 3D-2D and 3D-3D point correspondences on RGBD images
ossamaAhmed / Monocular Visual OdometryImplemented a monocular visual odometry (VO) pipeline with the most essential features: initialization of 3D landmarks, keypoint tracking between two frames, pose estimation using established 2D ↔ 3D correspondences, and triangulation of new land- marks.
