Automatic Alignment of Color Imagery onto 3D Laser Radar Data

Vasile, Alexandru N.; Waugh, Frederick R.; Greisokh, Daniel; Heinrichs, Richard

doi:10.1109/aipr.2006.16

Cited by 10 publications

(12 citation statements)

References 3 publications

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“…We additionally propose to refine the pose optimization by minimizing locally a densely computed self-similarity distance to accurately align local image regions where standard multi-modal similarity measures like MI or CR have major difficulties. The fusion of 2D images with LiDAR data is still an active research field [15][16][17]. The closest work [18] with respect to our application uses MI to register optical images with LiDAR data.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Accurate Single Image Multi-modal Camera Pose Estimation

Bodensteiner

Hebel

Arens

2012

Trends and Topics in Computer Vision

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Abstract. A well known problem in photogrammetry and computer vision is the precise and robust determination of camera poses with respect to a given 3D model. In this work we propose a novel multi-modal method for single image camera pose estimation with respect to 3D models with intensity information (e.g., LiDAR data with reflectance information). We utilize a direct point based rendering approach to generate synthetic 2D views from 3D datasets in order to bridge the dimensionality gap. The proposed method then establishes 2D/2D point and local region correspondences based on a novel self-similarity distance measure. Correct correspondences are robustly identified by searching for small regions with a similar geometric relationship of local self-similarities using a Generalized Hough Transform. After backprojection of the generated features into 3D a standard Perspective-n-Points problem is solved to yield an initial camera pose. The pose is then accurately refined using an intensity based 2D/3D registration approach. An evaluation on Vis/IR 2D and airborne and terrestrial 3D datasets shows that the proposed method is applicable to a wide range of different sensor types. In addition, the approach outperforms standard global multi-modal 2D/3D registration approaches based on Mutual Information with respect to robustness and speed. Potential applications are widespread and include for instance multispectral texturing of 3D models, SLAM applications, sensor data fusion and multi-spectral camera calibration and super-resolution applications.

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Section: Related Work and Contributionmentioning

confidence: 99%

Accurate Single Image Multi-modal Camera Pose Estimation

Bodensteiner

Hebel

Arens

2012

Trends and Topics in Computer Vision

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“…Frueh et al [4] used line segment matching to adjust initial camera parameters from GPS/INS by exhaustively searching camera position, orientation, and focal length. The method of Vasile et al [21] generates pseudo-intensity images with shadows from LiDAR to match 2D imagery. Then camera parameters from GPS and camera line of sight information are exhaustively estimated, similar to [4].…”

Section: Introductionmentioning

confidence: 99%

Automatic registration of LiDAR and optical imagery using depth map stereo

Kim

Correa

Max

2014

2014 IEEE International Conference on Computational Photography (ICCP)

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Automatic fusion of aerial optical imagery and untextured LiDAR data has been of significant interest for generating photo-realistic 3D urban models in recent years. However, unsupervised, robust registration still remains a challenge. This paper presents a new registration method that does not require priori knowledge such as GPS/INS information. The proposed algorithm is based on feature correspondence between a LiDAR depth map and a depth map from an optical image. Each optical depth map is generated from edge-preserving dense correspondence between the image and another optical image, followed by ground plane estimation and alignment for depth consistency. Our two-pass RANSAC with Maximum Likelihood estimation incorporates 2D-2D and 2D-3D correspondences to yield robust camera pose estimation. Experiments with a LiDARoptical imagery dataset show promising results, without using initial pose information.

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“…The dense 3D geometry used in these techniques allow for much more robust detection of geometric primitives such as edges and corners for matching. In the area of single-view registration, Vasile et al (2006) introduced LIDAR data to derive a pseudo-intensity image with shadows for correlation with aerial imagery. Their registration procedure starts with GPS and camera line of sight information and then uses an exhaustive search over translation, scale, and lens distortion.…”

Section: Introductionmentioning

confidence: 99%

Automatic registration of Iphone images to LASER point clouds of the urban structures using shape features

Sırmaçek

Lindenbergh

Menenti

2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Fusion of 3D airborne laser (LIDAR) data and terrestrial optical imagery can be applied in 3D urban modeling and model up-dating. The most challenging aspect of the fusion procedure is registering the terrestrial optical images on the LIDAR point clouds. In this article, we propose an approach for registering these two different data from different sensor sources. As we use iPhone camera images which are taken in front of the interested urban structure by the application user and the high resolution LIDAR point clouds of the acquired by an airborne laser sensor. After finding the photo capturing position and orientation from the iPhone photograph metafile, we automatically select the area of interest in the point cloud and transform it into a range image which has only grayscale intensity levels according to the distance from the image acquisition position. We benefit from local features for registering the iPhone image to the generated range image. In this article, we have applied the registration process based on local feature extraction and graph matching. Finally, the registration result is used for facade texture mapping on the 3D building surface mesh which is generated from the LIDAR point cloud. Our experimental results indicate possible usage of the proposed algorithm framework for 3D urban map updating and enhancing purposes.

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Automatic Alignment of Color Imagery onto 3D Laser Radar Data

Cited by 10 publications

References 3 publications

Accurate Single Image Multi-modal Camera Pose Estimation

Accurate Single Image Multi-modal Camera Pose Estimation

Automatic registration of LiDAR and optical imagery using depth map stereo

Automatic registration of Iphone images to LASER point clouds of the urban structures using shape features

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