Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction

Cheng, Jian; Leng, Cong; Wu, Jiaxiang; Cui, Hainan; Lu, Hanqing

doi:10.1109/cvpr.2014.8

Cited by 148 publications

(93 citation statements)

References 17 publications

(15 reference statements)

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“…The concrete implementations are: (i) BruteForce; (ii) ANNkD trees [19]; (iii) Cascade hashing [21]. They can be used to compute nearest 3D points or to find corresponding points of a scene by matching features across a series of image pairs.…”

Section: Feature and Image Collection Matchingmentioning

confidence: 99%

OpenMVG: Open Multiple View Geometry

Moulon

Monasse

Perrot

et al. 2017

Lecture Notes in Computer Science

286

251

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Abstract. The OpenMVG C++ library provides a vast collection of multipleview geometry tools and algorithms to spread the usage of computer vision and structure-from-motion techniques. Close to the state-of-the-art in its domain, it provides an easy access to common tools used in 3D reconstruction from images. Following the credo "Keep it simple, keep it maintainable" the library is designed as a modular collection of algorithms, libraries and binaries that can be used independently or as bricks to build larger systems. Thanks to its strict test driven development, the library is packaged with unit-test code samples that make the library easy to learn, modify and use. Since its first release in 2013 under the MPL2 license, OpenMVG has gathered an active community of users and contributors from many fields, spanning hobbyists, students, computer vision experts, and industry members.

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Section: Feature and Image Collection Matchingmentioning

confidence: 99%

OpenMVG: Open Multiple View Geometry

Moulon

Monasse

Perrot

et al. 2017

Lecture Notes in Computer Science

286

251

View full text Add to dashboard Cite

show abstract

“…Third, N is generated by calculating the difference between the value of id ptr [index ] and id ptr [index + 1 ] (line [8][9][10][11][12]. These computations are processed in thread parallel for each hash value of the query.…”

Section: Obtaining Matched Listmentioning

confidence: 99%

Efficient Position Estimation Basedon GPU-Accelerated Content-basedImage Retrieval

Kusamura

Amagasa

Kitagawa

et al. 2018

JMM

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AbstractsWe propose an efficient position estimation method based on GPUaccelerated content-based image retrieval (CBIR). The idea is to use videos of first-person vision associated with geographical position information as the database. When a user sends a current subjective image, the system estimates the position using CBIR. Since features extracted from images are in general high-dimensional vectors, thousands of vectors are extracted even from a single image, resulting in high processing cost. On the other hand, GPUs (graphics processing unit) have contributed to accelerate various processing, while they are originally for graphics processing. Therefore, we utilize GPU to accelerate CBIR with appropriate data structures and algorithms. Moreover, our proposed method considers spatial locality of pedestrians in position estimation applications in order to improve accuracy. Multimedia, Vol. 14 2, 197-230. doi: 10.13052/jmm1550-4646.1423 c 2018 We demonstrate the efficiency and accuracy of the proposed method through experiments using a video dataset. Journal of Mobile

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“…The three cameras start to take pictures of the same scene synchronously and transmit the image data to the image processing host through USB3.0 in real-time. Image processing host, first of all, utilizes the correlation algorithm [9,10] to deal with two color images and calculate the disparity data of two images. Second, the image processing host matches the 3D image data of the low-resolution depth camera with the left image data of the color cameras.…”

Section: Design Of the Systemmentioning

confidence: 99%

Vision System of Mobile Robot Combining Binocular and Depth Cameras

2017

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In order to optimize the three-dimensional (3D) reconstruction and obtain more precise actual distances of the object, a 3D reconstruction system combining binocular and depth cameras is proposed in this paper. The whole system consists of two identical color cameras, a TOF depth camera, an image processing host, a mobile robot control host, and a mobile robot. Because of structural constraints, the resolution of TOF depth camera is very low, which difficultly meets the requirement of trajectory planning. The resolution of binocular stereo cameras can be very high, but the effect of stereo matching is not ideal for low-texture scenes. Hence binocular stereo cameras also difficultly meet the requirements of high accuracy. In this paper, the proposed system integrates depth camera and stereo matching to improve the precision of the 3D reconstruction. Moreover, a double threads processing method is applied to improve the efficiency of the system. The experimental results show that the system can effectively improve the accuracy of 3D reconstruction, identify the distance from the camera accurately, and achieve the strategy of trajectory planning.

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Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction

Abstract: Abstract

Cited by 148 publications

References 17 publications

OpenMVG: Open Multiple View Geometry

OpenMVG: Open Multiple View Geometry

Efficient Position Estimation Basedon GPU-Accelerated Content-basedImage Retrieval

Vision System of Mobile Robot Combining Binocular and Depth Cameras

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