Pub Date : 2001-12-09DOI: 10.1109/SMBV.2001.988764
K. Ng, M. Trivedi, H. Ishiguro
In this paper a new "range-space" approach, for rendering visual models using a network of multiple omnidirectional vision sensors (ODVS) is presented. This integrated approach allows for simultaneous extraction of 3-D range as well as visual models. The approach requires three distinct steps of analyzing multiple ODVS video input streams: 1) Search, 2) Match, and 3) Render. At the output, a user-specified view is rendered. This three-step process does not require 3D model of the scene to be provided.
{"title":"Range-space approach for generalized multiple baseline stereo and direct virtual view synthesis","authors":"K. Ng, M. Trivedi, H. Ishiguro","doi":"10.1109/SMBV.2001.988764","DOIUrl":"https://doi.org/10.1109/SMBV.2001.988764","url":null,"abstract":"In this paper a new \"range-space\" approach, for rendering visual models using a network of multiple omnidirectional vision sensors (ODVS) is presented. This integrated approach allows for simultaneous extraction of 3-D range as well as visual models. The approach requires three distinct steps of analyzing multiple ODVS video input streams: 1) Search, 2) Match, and 3) Render. At the output, a user-specified view is rendered. This three-step process does not require 3D model of the scene to be provided.","PeriodicalId":204646,"journal":{"name":"Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114499185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2001-12-09DOI: 10.1109/SMBV.2001.988765
Peter Peer, F. Solina
In this article we present a panoramic depth imaging system. The system is mosaic-based which means that we use a single rotating camera and assemble the captured images in a mosaic. Due to a, setoff of the camera's optical center from the rotational center of the system we are able to capture the motion parallax effect which enables the stereo reconstruction. The camera is rotating on a circular path with the step defined by an angle, equivalent to one column of the captured image. The equation for depth estimation can be easily extracted from system geometry. To find the corresponding points on a stereo pair of panoramic images the epipolar geometry needs to be determined. It can be shown that the epipolar geometry is very simple if we are doing the reconstruction based on a symmetric pair of stereo panoramic images. We get a symmetric pair of stereo panoramic images when we take symmetric columns on the left and on the right side from the captured image center column. Epipolar lines of the symmetrical pair of panoramic images are image rows. We focused mainly on the system analysis. Results of the stereo reconstruction procedure and quality evaluation of generated depth images are quite promising. The system performs well in the reconstruction of small indoor spaces. Our final goal is to develop a system for automatic navigation of a mobile robot in a room.
{"title":"Mosaic-based panoramic depth imaging with a single standard camera","authors":"Peter Peer, F. Solina","doi":"10.1109/SMBV.2001.988765","DOIUrl":"https://doi.org/10.1109/SMBV.2001.988765","url":null,"abstract":"In this article we present a panoramic depth imaging system. The system is mosaic-based which means that we use a single rotating camera and assemble the captured images in a mosaic. Due to a, setoff of the camera's optical center from the rotational center of the system we are able to capture the motion parallax effect which enables the stereo reconstruction. The camera is rotating on a circular path with the step defined by an angle, equivalent to one column of the captured image. The equation for depth estimation can be easily extracted from system geometry. To find the corresponding points on a stereo pair of panoramic images the epipolar geometry needs to be determined. It can be shown that the epipolar geometry is very simple if we are doing the reconstruction based on a symmetric pair of stereo panoramic images. We get a symmetric pair of stereo panoramic images when we take symmetric columns on the left and on the right side from the captured image center column. Epipolar lines of the symmetrical pair of panoramic images are image rows. We focused mainly on the system analysis. Results of the stereo reconstruction procedure and quality evaluation of generated depth images are quite promising. The system performs well in the reconstruction of small indoor spaces. Our final goal is to develop a system for automatic navigation of a mobile robot in a room.","PeriodicalId":204646,"journal":{"name":"Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121790122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2001-12-09DOI: 10.1109/SMBV.2001.988762
Changming Sun
This paper presents a fast and reliable stereo matching algorithm which produces a dense disparity map by using fast cross correlation, rectangular subregioning and 3D maximum-surface techniques in a coarse-to-fine scheme. Fast correlation is achieved by using the box filtering technique whose speed is invariant to the size of correlation window and by segmenting the stereo images at different levels of the pyramid into rectangular subimages. The disparity for the whole image is found in the 3D correlation coefficient volume by obtaining the maximum-surface using our novel two-stage dynamic programming technique. There are two original contributions in this paper: (1) development of a rectangular subregioning (RSR) technique for fast similarity measure; and (2) development of a novel two-stage dynamic programming (STDP) technique for obtaining 3D maximum surface in a 3D volume efficiently. Typical running time of our algorithm implemented in C language on a 512/spl times/512 image is in the order of a few seconds. A variety of synthetic and real images have been tested, and good results have been obtained.
{"title":"Rectangular subregioning and 3-D maximum-surface techniques for fast stereo matching","authors":"Changming Sun","doi":"10.1109/SMBV.2001.988762","DOIUrl":"https://doi.org/10.1109/SMBV.2001.988762","url":null,"abstract":"This paper presents a fast and reliable stereo matching algorithm which produces a dense disparity map by using fast cross correlation, rectangular subregioning and 3D maximum-surface techniques in a coarse-to-fine scheme. Fast correlation is achieved by using the box filtering technique whose speed is invariant to the size of correlation window and by segmenting the stereo images at different levels of the pyramid into rectangular subimages. The disparity for the whole image is found in the 3D correlation coefficient volume by obtaining the maximum-surface using our novel two-stage dynamic programming technique. There are two original contributions in this paper: (1) development of a rectangular subregioning (RSR) technique for fast similarity measure; and (2) development of a novel two-stage dynamic programming (STDP) technique for obtaining 3D maximum surface in a 3D volume efficiently. Typical running time of our algorithm implemented in C language on a 512/spl times/512 image is in the order of a few seconds. A variety of synthetic and real images have been tested, and good results have been obtained.","PeriodicalId":204646,"journal":{"name":"Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115499876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2001-12-09DOI: 10.1109/SMBV.2001.988759
Minglun Gong, Herbert Yang
In this paper, a new genetic-based stereo matching algorithm is presented. Our motivation is to improve the accuracy of the disparity map generated by removing the mismatches caused by both occlusions and false targets. In our approach, the stereo matching problem is considered as an optimization problem. The algorithm first takes advantage of multi-view stereo images to detect occlusions, therefore, removes mismatches caused by visibility problems. A genetic algorithm is then used to optimize both the compatibility between corresponding points and the continuity of the disparity map, which removes mismatches caused false targets. In addition, the quadtree structure is used to implement a multiresolution framework. Since nodes at different level of the quadtree cover different number of pixels, selecting nodes at different levels gives similar effect as adjusting the window size at different locations of the image. The experimental results show that our approach can generate more accurate disparity maps than two existing approaches.
{"title":"Multi-resolution stereo matching using genetic algorithm","authors":"Minglun Gong, Herbert Yang","doi":"10.1109/SMBV.2001.988759","DOIUrl":"https://doi.org/10.1109/SMBV.2001.988759","url":null,"abstract":"In this paper, a new genetic-based stereo matching algorithm is presented. Our motivation is to improve the accuracy of the disparity map generated by removing the mismatches caused by both occlusions and false targets. In our approach, the stereo matching problem is considered as an optimization problem. The algorithm first takes advantage of multi-view stereo images to detect occlusions, therefore, removes mismatches caused by visibility problems. A genetic algorithm is then used to optimize both the compatibility between corresponding points and the continuity of the disparity map, which removes mismatches caused false targets. In addition, the quadtree structure is used to implement a multiresolution framework. Since nodes at different level of the quadtree cover different number of pixels, selecting nodes at different levels gives similar effect as adjusting the window size at different locations of the image. The experimental results show that our approach can generate more accurate disparity maps than two existing approaches.","PeriodicalId":204646,"journal":{"name":"Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001)","volume":"250 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134312962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2001-12-09DOI: 10.1109/SMBV.2001.988775
G. Van Meerbergen, M. Vergauwen, M. Pollefeys, L. Van Gool
In this paper, a new hierarchical stereo algorithm is presented. The algorithm matches individual pixels in corresponding scanlines by minimizing a cost function. Several cost functions are compared. The algorithm achieves a tremendous gain in speed and memory requirements by implementing it hierarchically. The images are down sampled an optimal number of times and the disparity map of a lower level is used as 'offset' disparity map at a higher level. An important contribution consists of the complexity analysis of the algorithm. It is shown that this complexity is independent of the disparity range. This result is also used to determine the-optimal number of down sample levels. This speed gain results in the ability to use more complex (compute intensive) cost functions that deliver high quality disparity maps. Another advantage of this algorithm is that cost functions can be chosen independent of the optimisation algorithm. Finally, the algorithm was carefully implemented so that a minimal amount of memory is used. It has proven its efficiency on large images with a high disparity range as well as its quality. Examples are given in this paper.
{"title":"A hierarchical stereo algorithm using dynamic programming","authors":"G. Van Meerbergen, M. Vergauwen, M. Pollefeys, L. Van Gool","doi":"10.1109/SMBV.2001.988775","DOIUrl":"https://doi.org/10.1109/SMBV.2001.988775","url":null,"abstract":"In this paper, a new hierarchical stereo algorithm is presented. The algorithm matches individual pixels in corresponding scanlines by minimizing a cost function. Several cost functions are compared. The algorithm achieves a tremendous gain in speed and memory requirements by implementing it hierarchically. The images are down sampled an optimal number of times and the disparity map of a lower level is used as 'offset' disparity map at a higher level. An important contribution consists of the complexity analysis of the algorithm. It is shown that this complexity is independent of the disparity range. This result is also used to determine the-optimal number of down sample levels. This speed gain results in the ability to use more complex (compute intensive) cost functions that deliver high quality disparity maps. Another advantage of this algorithm is that cost functions can be chosen independent of the optimisation algorithm. Finally, the algorithm was carefully implemented so that a minimal amount of memory is used. It has proven its efficiency on large images with a high disparity range as well as its quality. Examples are given in this paper.","PeriodicalId":204646,"journal":{"name":"Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115117372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2001-12-09DOI: 10.1109/SMBV.2001.988773
O. Veksler
We present a new feature based algorithm for stereo correspondence. Most of the previous feature based methods match sparse features like edge pixels, producing only sparse disparity maps. Our algorithm detects and matches dense features between the left and right images of a stereo pair, producing a semi-dense disparity map. Our dense feature is defined with respect to both images of a stereo pair, and it is computed during the stereo matching process, not a preprocessing step. In essence, a dense feature is a connected set of pixels in the left image and a corresponding set of pixels in the right image such that the intensity edges on the boundary of these sets are stronger than their matching error (which is basically the difference in intensities between corresponding boundary pixels). Our algorithm produces accurate semi-dense disparity maps, leaving featureless regions in the scene unmatched. It is robust, requires little parameter tuning, can handle brightness differences between images, and is fast (linear complexity).
{"title":"Semi-dense stereo correspondence with dense features","authors":"O. Veksler","doi":"10.1109/SMBV.2001.988773","DOIUrl":"https://doi.org/10.1109/SMBV.2001.988773","url":null,"abstract":"We present a new feature based algorithm for stereo correspondence. Most of the previous feature based methods match sparse features like edge pixels, producing only sparse disparity maps. Our algorithm detects and matches dense features between the left and right images of a stereo pair, producing a semi-dense disparity map. Our dense feature is defined with respect to both images of a stereo pair, and it is computed during the stereo matching process, not a preprocessing step. In essence, a dense feature is a connected set of pixels in the left image and a corresponding set of pixels in the right image such that the intensity edges on the boundary of these sets are stronger than their matching error (which is basically the difference in intensities between corresponding boundary pixels). Our algorithm produces accurate semi-dense disparity maps, leaving featureless regions in the scene unmatched. It is robust, requires little parameter tuning, can handle brightness differences between images, and is fast (linear complexity).","PeriodicalId":204646,"journal":{"name":"Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131350814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2001-12-09DOI: 10.1109/SMBV.2001.988769
Sang Hwa Lee, Y. Kanatsugu, Jong-Il Park
This paper proposes a stochastic approach to estimate the disparity field combined with line field. In the maximum a posteriori (MAP) method based on Markov random field (MRF) model, it is important to optimize and converge the Gibbs potential function corresponding to the perturbed disparity field. The proposed optimization method, stochastic diffusion, takes advantage of the probabilistic distribution of the neighborhood fields, and diffuses the Gibbs potential space to be stable iteratively. By using the neighborhood distribution in the non-random and non-deterministic diffusion, the stochastic diffusion improves both the estimation accuracy and the convergence speed. In the paper, the hierarchical stochastic diffusion is also applied to the disparity field. The hierarchical approach reduces the memory and computational load, and increases the convergence of the potential space. The line field is the discontinuity model of the disparity field. The paper also proposes an effective configuration of the neighborhood to be suitable for the hierarchical disparity structure. According to the experiments, the stochastic diffusion shows good estimation performance. The line field improves the estimation at the object boundary, and the estimated line field coincides with the object boundary with the useful contours. Furthermore, the stochastic diffusion with line field embeds the occlusion detection and compensation. And, the stochastic diffusion converges the estimated fields very fast in the hierarchical scheme. The stochastic diffusion is applicable to any kind of field estimation given the appropriate definition of the field and MRF models.
{"title":"Hierarchical stochastic diffusion for disparity estimation","authors":"Sang Hwa Lee, Y. Kanatsugu, Jong-Il Park","doi":"10.1109/SMBV.2001.988769","DOIUrl":"https://doi.org/10.1109/SMBV.2001.988769","url":null,"abstract":"This paper proposes a stochastic approach to estimate the disparity field combined with line field. In the maximum a posteriori (MAP) method based on Markov random field (MRF) model, it is important to optimize and converge the Gibbs potential function corresponding to the perturbed disparity field. The proposed optimization method, stochastic diffusion, takes advantage of the probabilistic distribution of the neighborhood fields, and diffuses the Gibbs potential space to be stable iteratively. By using the neighborhood distribution in the non-random and non-deterministic diffusion, the stochastic diffusion improves both the estimation accuracy and the convergence speed. In the paper, the hierarchical stochastic diffusion is also applied to the disparity field. The hierarchical approach reduces the memory and computational load, and increases the convergence of the potential space. The line field is the discontinuity model of the disparity field. The paper also proposes an effective configuration of the neighborhood to be suitable for the hierarchical disparity structure. According to the experiments, the stochastic diffusion shows good estimation performance. The line field improves the estimation at the object boundary, and the estimated line field coincides with the object boundary with the useful contours. Furthermore, the stochastic diffusion with line field embeds the occlusion detection and compensation. And, the stochastic diffusion converges the estimated fields very fast in the hierarchical scheme. The stochastic diffusion is applicable to any kind of field estimation given the appropriate definition of the field and MRF models.","PeriodicalId":204646,"journal":{"name":"Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123875310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2001-12-09DOI: 10.1109/SMBV.2001.988767
J. Shao
The emergence of a new generation of 3D auto stereoscopic displays is driving the requirement for multi-baseline images. The dominant form of this display technology requires multiple views of the same scene, captured at a single, instance in time along a common baseline in order to project stereoscopic images to the viewer. The direct acquisition of multiple views (typically 8 or 16 for the current generation of such displays) is problematic due to the difficulty of configuring, calibrating and controlling multiple cameras simultaneously. This paper describes a technique that alleviates these problems by generating the required views from binocular images. Considering each stereo pair in isolation leads to inconsistency on image sequences. By incorporating a motion-tracking algorithm this problem is significantly reduced. In this paper we describe a novel approach to stereo matching on image sequences for the purpose of generating multiple virtual camera views. Results of extensive tests on stereo image sequences, will be documented indicating that this approach is promising both in terms of the speed of execution and the quality of the results produced.
{"title":"Combination of stereo, motion and rendering for 3D footage display","authors":"J. Shao","doi":"10.1109/SMBV.2001.988767","DOIUrl":"https://doi.org/10.1109/SMBV.2001.988767","url":null,"abstract":"The emergence of a new generation of 3D auto stereoscopic displays is driving the requirement for multi-baseline images. The dominant form of this display technology requires multiple views of the same scene, captured at a single, instance in time along a common baseline in order to project stereoscopic images to the viewer. The direct acquisition of multiple views (typically 8 or 16 for the current generation of such displays) is problematic due to the difficulty of configuring, calibrating and controlling multiple cameras simultaneously. This paper describes a technique that alleviates these problems by generating the required views from binocular images. Considering each stereo pair in isolation leads to inconsistency on image sequences. By incorporating a motion-tracking algorithm this problem is significantly reduced. In this paper we describe a novel approach to stereo matching on image sequences for the purpose of generating multiple virtual camera views. Results of extensive tests on stereo image sequences, will be documented indicating that this approach is promising both in terms of the speed of execution and the quality of the results produced.","PeriodicalId":204646,"journal":{"name":"Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120957936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/SMBV.2001.988774
M. Okutomi, Y. Katayama
In area-based stereo matching, there is a problem called "boundary overreach", i.e. the recovered object boundary turns out to be wrongly located away from the real one. This is especially harmful to segmenting objects using depth information. A few approaches have been proposed to solve this problem. However, these techniques tend to degrade on smooth surfaces. That is, there seems to be a trade-off problem between recovering precise object edges and obtaining smooth surfaces. In this paper, we propose a new simple method to solve this problem. Using multiple stereo pairs and multiple windowing, our method detects the region where the boundary overreach is likely to occur (let us call it "BO region") and adopts appropriate methods for the BO and non-BO regions. Although the proposed method is quite simple, the experimental results have shown that it is very effective at recovering both sharp object edges at their correct locations and smooth object surfaces.
{"title":"A simple stereo algorithm to recover precise object boundaries and smooth surfaces","authors":"M. Okutomi, Y. Katayama","doi":"10.1109/SMBV.2001.988774","DOIUrl":"https://doi.org/10.1109/SMBV.2001.988774","url":null,"abstract":"In area-based stereo matching, there is a problem called \"boundary overreach\", i.e. the recovered object boundary turns out to be wrongly located away from the real one. This is especially harmful to segmenting objects using depth information. A few approaches have been proposed to solve this problem. However, these techniques tend to degrade on smooth surfaces. That is, there seems to be a trade-off problem between recovering precise object edges and obtaining smooth surfaces. In this paper, we propose a new simple method to solve this problem. Using multiple stereo pairs and multiple windowing, our method detects the region where the boundary overreach is likely to occur (let us call it \"BO region\") and adopts appropriate methods for the BO and non-BO regions. Although the proposed method is quite simple, the experimental results have shown that it is very effective at recovering both sharp object edges at their correct locations and smooth object surfaces.","PeriodicalId":204646,"journal":{"name":"Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129186602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/SMBV.2001.988763
T. Sato, M. Kanbara, N. Yokoya, I. Takemura
Three-dimensional (3-D) models of outdoor scenes are widely used for object recognition, navigation, mixed reality, and so on. Because such models are often made manually with high costs, automatic and dense 3-D reconstruction is widely investigated. In related work, a dense 3-D model is generated by using a stereo method. However these methods cannot use several hundreds images together for dense depth estimation because it is difficult to accurately calibrate a large number of cameras. In this paper we propose a dense 3-D reconstruction method that first estimates extrinsic camera parameters of a hand-held video camera, and then reconstructs a dense 3-D model of a scene. We can acquire a model of the scene accurately by using several hundreds input images.
{"title":"Dense 3-D reconstruction of an outdoor scene, by hundreds-baseline stereo using a hand-held video camera","authors":"T. Sato, M. Kanbara, N. Yokoya, I. Takemura","doi":"10.1109/SMBV.2001.988763","DOIUrl":"https://doi.org/10.1109/SMBV.2001.988763","url":null,"abstract":"Three-dimensional (3-D) models of outdoor scenes are widely used for object recognition, navigation, mixed reality, and so on. Because such models are often made manually with high costs, automatic and dense 3-D reconstruction is widely investigated. In related work, a dense 3-D model is generated by using a stereo method. However these methods cannot use several hundreds images together for dense depth estimation because it is difficult to accurately calibrate a large number of cameras. In this paper we propose a dense 3-D reconstruction method that first estimates extrinsic camera parameters of a hand-held video camera, and then reconstructs a dense 3-D model of a scene. We can acquire a model of the scene accurately by using several hundreds input images.","PeriodicalId":204646,"journal":{"name":"Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001)","volume":"281 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129699432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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