Refined non-rigid registration of a panoramic image sequence to a LiDAR point cloud

Authors:
Arjen Swart;Jonathan Broere;Remco Veltkamp;Robby Tan
Affiliations:
Cyclomedia Technology BV, Waardenburg, The Netherlands and Utrecht University, Utrecht, The Netherlands;Cyclomedia Technology BV, Waardenburg, The Netherlands;Utrecht University, Utrecht, The Netherlands;Utrecht University, Utrecht, The Netherlands
Venue:
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
Year:
2011

Citing 7
Cited 0

A Method for Registration of 3-D Shapes

IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Using Photo-Consistency to Register 2D Optical Images of the Human Face to a 3D Surface Model

IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Automatic 3D to 2D Registration for the Photorealistic Rendering of Urban Scenes

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Alignment of Continuous Video onto 3D Point Clouds

IEEE Transactions on Pattern Analysis and Machine Intelligence
SBA: A software package for generic sparse bundle adjustment

ACM Transactions on Mathematical Software (TOMS)
Automatic registration of color images to 3D geometry

Proceedings of the 2009 Computer Graphics International Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

The combination of LiDAR data with panoramic images could be of great benefit to many geo-related applications and processes such as measuring and map making. Although it is possible to record both LiDAR points and panoramic images at the same time, there are economic and practical advantages to separating the acquisition of both types of data. However, when LiDAR and image data is recorded separately, poor GPS reception in many urban areas will make registration between the data sets necessary. In this paper, we describe a method to register a sequence of panoramic images to a LiDAR point cloud using a non-rigid version of ICP that incorporates a bundle adjustment framework. The registration is then refined by integrating image-to-reflectance data SIFT correspondences into the bundle adjustment. We demonstrate the validity of this registration method by a comparison against ground truth data.