Simultaneous localization, mapping and deblurring

Lee, Hee-Seok; Junghyun, Kwon; Lee, Kyoung Mu

doi:10.1109/iccv.2011.6126370

Cited by 47 publications

(31 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Stamos et al [16] presents some metrics to estimate the amount of blur in image sequence, based on color saturation, local auto-correlation and gradient distribution. Feature tracking and camera poses recovery methods in blurry image sequences can be improved using edgelets [17] or blurring the previous frame in order to obtain a consistent tracking [18] or deblurring a current frame with a blur kernel [19]. Guidi et al, [14] analyses how image pre-processing with polarizing filters and HDR imaging may improve indoor automated 3D reconstruction processes based on SfM methods.…”

Section: Related Workmentioning

confidence: 99%

An Advanced Pre-Processing Pipeline to Improve Automated Photogrammetric Reconstructions of Architectural Scenes

et al. 2016

View full text Add to dashboard Cite

Automated image-based 3D reconstruction methods are more and more flooding our 3D modeling applications. Fully automated solutions give the impression that from a sample of randomly acquired images we can derive quite impressive visual 3D models. Although the level of automation is reaching very high standards, image quality is a fundamental pre-requisite to produce successful and photo-realistic 3D products, in particular when dealing with large datasets of images. This article presents an efficient pipeline based on color enhancement, image denoising, color-to-gray conversion and image content enrichment. The pipeline stems from an analysis of various state-of-the-art algorithms and aims to adjust the most promising methods, giving solutions to typical failure causes. The assessment evaluation proves how an effective image pre-processing, which considers the entire image dataset, can improve the automated orientation procedure and dense 3D point cloud reconstruction, even in the case of poor texture scenarios.

show abstract

Section: Related Workmentioning

confidence: 99%

An Advanced Pre-Processing Pipeline to Improve Automated Photogrammetric Reconstructions of Architectural Scenes

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Our work is related to works on tracking in the presence of motion blur [13,16,19,20]. Of particular importance are [13,19], which use the commutativity of the blur operation to match blurred images and avoid deblurring.…”

Section: Related Workmentioning

confidence: 99%

Optical flow in the presence of spatially-varying motion blur

Portz

Zhang

Jiang

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

View full text Add to dashboard Cite

This paper extends the classical warping-based optical flow method to achieve accurate flow in the presence of spatially-varying motion blur. Our idea is to parameterize the appearance of each frame as a function of both the pixel motion and the motion-induced blur. We search for the flows that best match two consecutive frames, which amounts to finding the derivative of a blurred frame with respect to both the motion and the blur, where the blur itself is a function of the motion. We propose an efficient technique to calculate the derivatives using pre-filtering. Our technique avoids performing spatially-varying filtering (which can be computationally expensive) during the optimization iterations. In the end, our derivative calculation technique can be easily incorporated with classical flow code to handle video with non-uniform motion blur with little performance penalty. Our method is evaluated on both synthetic and real videos and outperforms conventional flow methods in the presence of motion blur.

show abstract

“…No further methods are included in the evaluation, since no other similar methods exist to the best of our knowledge, and a comparison with keyframe selection strategies (Seo et al, 2008) or deblurring methods (Lee et al, 2011) would be unfair as they differ in purposes and use additional information. In particular, DWAFS aims at providing a fast data preprocessing to be used for other tasks, working on a simple gradient statistic, that does not require complex time-consuming image processing, as the computation of image feature keypoints, previous poses and 3D structure as most of keyframe selectors and deblurring methods, which represent possible final tasks which can benefit of DWARF.…”

Section: Fastadaptiveframepreprocessingfor3dreconstructionmentioning

confidence: 99%

“…Different methods have been presented to improve the camera re-localization and recovery system and to allow a robust tracking of blurry features. These include the use of edgelets (Klein and Murray, 2008) and the estimation of the blur kernel to deblur the current frame (Lee et al, 2011), incorporating camera trajectory clues with blind deconvolution techniques (Joshi et al, 2008), or to blur the previous frame in order to obtain a consistent tracking (Mei and Reid, 2008).…”

Section: Introductionmentioning

confidence: 99%

Fast Adaptive Frame Preprocessing for 3D Reconstruction

Bellavia

Fanfani

Colombo

2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

View full text Add to dashboard Cite

Abstract:This paper presents a new online preprocessing strategy to detect and discard ongoing bad frames in video sequences. These include frames where an accurate localization between corresponding points is difficult, such as for blurred frames, or which do not provide relevant information with respect to the previous frames in terms of texture, image contrast and non-flat areas. Unlike keyframe selectors and deblurring methods, the proposed approach is a fast preprocessing working on a simple gradient statistic, that does not require to compute complex time-consuming image processing, such as the computation of image feature keypoints, previous poses and 3D structure, or to know a priori the input sequence. The presented method provides a fast and useful frame pre-analysis which can be used to improve further image analysis tasks, including also the keyframe selection or the blur detection, or to directly filter the video sequence as shown in the paper, improving the final 3D reconstruction by discarding noisy frames and decreasing the final computation time by removing some redundant frames. This scheme is adaptive, fast and works at runtime by exploiting the image gradient statistic of the last few frames of the video sequence. Experimental results show that the proposed frame selection strategy is robust and improves the final 3D reconstruction both in terms of number of obtained 3D points and reprojection error, also reducing the computational time.

show abstract

Simultaneous localization, mapping and deblurring

Abstract: Handling motion blur is one of important issues in vi-

Cited by 47 publications

References 18 publications

An Advanced Pre-Processing Pipeline to Improve Automated Photogrammetric Reconstructions of Architectural Scenes

An Advanced Pre-Processing Pipeline to Improve Automated Photogrammetric Reconstructions of Architectural Scenes

Optical flow in the presence of spatially-varying motion blur

Fast Adaptive Frame Preprocessing for 3D Reconstruction

Contact Info

Product

Resources

About