Automatic registration of LiDAR and optical imagery using depth map stereo

No abstract is available for this paper.

• Publication year

  2014

• Venue

  International Conference on Computational Photography

• Publication date

  2014-05-02

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1109/ICCPHOT.2014.6831821
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• LSD: a Line Segment Detector

  2012cited by this paper
• A robust approach for automatic registration of aerial images with untextured aerial LiDAR data

  2009cited by this paper
• Automatic registration of LIDAR and optical images of urban scenes

  2009cited by this paper
• PatchMatch: a randomized correspondence algorithm for structural image editing

  2009cited by this paper
• Modeling the World from Internet Photo Collections

  2008cited by this paper
• Automatic registration of aerial imagery with untextured 3D LiDAR models

  2008cited by this paper
• Speeded-Up Robust Features (SURF)

  2008cited by this paper
• Integrating Automated Range Registration with Multiview Geometry for the Photorealistic Modeling of Large-Scale Scenes

  2008cited by this paper
• Semiautomatic registration between ground-level panoramas and an orthorectified aerial image for building modeling

  2007cited by this paper
• Estimating the Location of a Camera with Respect to a 3D Model

  2007cited by this paper
• Multiview Geometry for Texture Mapping 2D Images Onto 3D Range Data

  2006cited by this paper
• SURF: Speeded Up Robust Features

  2006influential reference
• Automatic Alignment of Color Imagery onto 3D Laser Radar Data

  2006cited by this paper
• Automatic 3D to 2D registration for the photorealistic rendering of urban scenes

  2005cited by this paper
• A Shadow Based Method for Image to Model Registration

  2004cited by this paper
• Automated texture mapping of 3D city models with oblique aerial imagery

  2004cited by this paper
• Alignment of continuous video onto 3D point clouds

  2004cited by this paper
• Geometry and Texture Recovery of Scenes of Large Scale

  2002cited by this paper
• Automatic Integration of Facade Textures into 3D Building Models with a Projective Geometry Based Line Clustering

  2002cited by this paper
• Mapping textures on 3D geometric model using reflectance image

  2001cited by this paper
• Multiple View Geometry in Computer Vision

  2001cited by this paper
• MLESAC: A New Robust Estimator with Application to Estimating Image Geometry

  2000cited by this paper
• Object recognition from local scale-invariant features

  1999cited by this paper
• Bilateral filtering for gray and color images

  1998cited by this paper
• Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

  1981cited by this paper
• Computer Vision and Image Understanding 116 (2012) 25–37 Contents lists available at SciVerse ScienceDirect Computer Vision and Image Understanding

  year unknowninfluential reference

• Simple and efficient registration of 3D point cloud and image data for an indoor mobile mapping system.

  2021cites this paper
• HyperMap: Compressed 3D Map for Monocular Camera Registration

  2021cites this paper
• A Simple and Efficient Registration of 3D Point Cloud and Image Data for Indoor Mobile Mapping System

  2020cites this paper
• A flexible targetless LiDAR–GNSS/INS–camera calibration method for UAV platforms

  2020cites this paper
• Registration of Aerial Optical Images with LiDAR Data Using the Closest Point Principle and Collinearity Equations

  2018influential citation
• Automatic segmentation of human depth map based on semantic segmentation of FCN and depth segmentation

  2018cites this paper
• Sensor Fusion and Registration of Lidar and Stereo Camera without Calibration Objects

  2017cites this paper
• Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest

  2016cites this paper
• Processing of Extremely High Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest—Part B: 3-D Contest

  2016cites this paper
• Automatic calibration and registration of lidar and stereo camera without calibration objects

  2015cites this paper
• Line Segment-based Aerial Image Registration

  2014cites this paper
• Multi-modal sensor registration for vehicle perception via deep neural networks

  2014cites this paper
• DomainMultionA Comparative Assessment of Selected Algorithms Registration of Remote Sensing Images

  year unknowncites this paper