The current paper addresses the problem of object identification from multiple3D partial views, collected from different view angles with the objective of disambiguating between similar objects. We assume a mobile robot equipped with a depth sensor that autonomously collects observations from an object from different positions, with no previous known pattern. The challenge is to efficiently combine the set of observations into a single classification. We approach the problem with a multiple hypothesis filter that allows to combine information from a sequence of observations given the robot movement. We further innovate by off-line learning neighborhoods between possible hypothesis based on the similarity of observations. Such neighborhoods translate directly the ambiguity between objects, and allow to transfer the knowledge of one object to the other. In this paper we introduce our algorithm, Multiple Hypothesis for Object Class Disambiguation from Multiple Observations, and evaluate its accuracy and efficiency.
{"title":"Multiple Hypothesis for Object Class Disambiguation from Multiple Observations","authors":"Susana Brandão, M. Veloso, J. Costeira","doi":"10.1109/3DV.2014.101","DOIUrl":"https://doi.org/10.1109/3DV.2014.101","url":null,"abstract":"The current paper addresses the problem of object identification from multiple3D partial views, collected from different view angles with the objective of disambiguating between similar objects. We assume a mobile robot equipped with a depth sensor that autonomously collects observations from an object from different positions, with no previous known pattern. The challenge is to efficiently combine the set of observations into a single classification. We approach the problem with a multiple hypothesis filter that allows to combine information from a sequence of observations given the robot movement. We further innovate by off-line learning neighborhoods between possible hypothesis based on the similarity of observations. Such neighborhoods translate directly the ambiguity between objects, and allow to transfer the knowledge of one object to the other. In this paper we introduce our algorithm, Multiple Hypothesis for Object Class Disambiguation from Multiple Observations, and evaluate its accuracy and efficiency.","PeriodicalId":275516,"journal":{"name":"2014 2nd International Conference on 3D Vision","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126157719","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}
K. Hansen, Jeppe Pedersen, Thomas Sølund, H. Aanæs, D. Kraft
A current trend in industrial automation implies a need for doing automatic scene understanding, from optical 3D sensors, which in turn imposes a need for a lightweight and reliable 3D optical sensor to be mounted on a collaborative robot e.g., Universal Robot UR5 or Kuka LWR. Here, we empirically evaluate the feasibility of structured light scanners for this purpose, by presenting a system optimized for this task. The system incorporates several recent advances in structured light scanning, such as Large-Gap Gray encoding for dealing with defocusing, automatic creation of illumination masks for noise removal, as well as employing a multi exposure approach dealing with different surface reflectance properties. In addition to this, we investigate expanding the traditional structured light setup to using three cameras, instead of one or two. Also, a novel method for fusing multiple exposures and camera pairs is given. We present an in-depth evaluation, that lead us to conclude, that this setup performs well on tasks relevant for an industrial environment, where many metallic and other surfaces with difficult reflectance properties are in abundance. We demonstrate, that the added components contribute to the robustness of the system. Hereby, we demonstrate that structured light scanning is a technology well suited for hyper flexible industrial automation, by proposing an appropriate system.
{"title":"A Structured Light Scanner for Hyper Flexible Industrial Automation","authors":"K. Hansen, Jeppe Pedersen, Thomas Sølund, H. Aanæs, D. Kraft","doi":"10.1109/3DV.2014.53","DOIUrl":"https://doi.org/10.1109/3DV.2014.53","url":null,"abstract":"A current trend in industrial automation implies a need for doing automatic scene understanding, from optical 3D sensors, which in turn imposes a need for a lightweight and reliable 3D optical sensor to be mounted on a collaborative robot e.g., Universal Robot UR5 or Kuka LWR. Here, we empirically evaluate the feasibility of structured light scanners for this purpose, by presenting a system optimized for this task. The system incorporates several recent advances in structured light scanning, such as Large-Gap Gray encoding for dealing with defocusing, automatic creation of illumination masks for noise removal, as well as employing a multi exposure approach dealing with different surface reflectance properties. In addition to this, we investigate expanding the traditional structured light setup to using three cameras, instead of one or two. Also, a novel method for fusing multiple exposures and camera pairs is given. We present an in-depth evaluation, that lead us to conclude, that this setup performs well on tasks relevant for an industrial environment, where many metallic and other surfaces with difficult reflectance properties are in abundance. We demonstrate, that the added components contribute to the robustness of the system. Hereby, we demonstrate that structured light scanning is a technology well suited for hyper flexible industrial automation, by proposing an appropriate system.","PeriodicalId":275516,"journal":{"name":"2014 2nd International Conference on 3D Vision","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128624983","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}
This paper proposes a hybrid software-hardware high-resolution projection system for 3D imaging based on fringe projection. The proposed solution combines the advantages of a digital projection with those of an analogue one. It is programmable and allows a high projection rate by avoiding mechanical displacements in the projection system. Moreover, it does not suffer from the limitations of digital systems such as the presence of inter-pixel gaps and limited resolution. The proposed projection system is relatively inexpensive to build since it is composed of a simple arrangement off-the-shelf components. The system is a combination of a low-resolution digital device such as a DMD, LCoS or LCD, some optical components and software to generate the fringe patterns. A prototype of a 3D scanner based on the proposed projection system is used to asses the fitness of the proposed technology.
{"title":"High Resolution Projector for 3D Imaging","authors":"M. Drouin, F. Blais, G. Godin","doi":"10.1109/3DV.2014.86","DOIUrl":"https://doi.org/10.1109/3DV.2014.86","url":null,"abstract":"This paper proposes a hybrid software-hardware high-resolution projection system for 3D imaging based on fringe projection. The proposed solution combines the advantages of a digital projection with those of an analogue one. It is programmable and allows a high projection rate by avoiding mechanical displacements in the projection system. Moreover, it does not suffer from the limitations of digital systems such as the presence of inter-pixel gaps and limited resolution. The proposed projection system is relatively inexpensive to build since it is composed of a simple arrangement off-the-shelf components. The system is a combination of a low-resolution digital device such as a DMD, LCoS or LCD, some optical components and software to generate the fringe patterns. A prototype of a 3D scanner based on the proposed projection system is used to asses the fitness of the proposed technology.","PeriodicalId":275516,"journal":{"name":"2014 2nd International Conference on 3D Vision","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125650812","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}
Jian Wang, A. Borsdorf, B. Heigl, T. Köhler, J. Hornegger
In interventional radiology, preoperative 3-D volumes can be fused with intra-operative 2-D fluoroscopic images. Since the accuracy is crucial to the clinical usability of image fusion, patient motion resulting in misalignments has to be corrected during the procedure. In this paper, a novel gradient based differential approach is proposed to estimate the 3-D rigid motion from the 2-D tracking of contour points. The mathematical relationship between the 3-D differential motion and the 2-D motion is derived using the 3-D gradient, based on which a tracking-based motion compensation pipeline is introduced. Given the initial registration, the contour points are extracted and tracked along 2-D frames. The 3-D rigid motion is estimated using the iteratively re-weighted least square minimization to enhance the robustness. Our novel approach is evaluated on 10 datasets consisting of 1010 monoplane fluoroscopic images of a thorax phantom with 3-D rigid motion. Over all datasets, the maximum structure shift in the 2-D projection caused by the 3-D motion varies from 17.3 mm to 33.2 mm. Our approach reduces the 2-D structure shift to the range of 1.93 mm to 6.52 mm. For the most challenging longitudinal off-plane rotation, our approach achieves an average coverage of 79.9% regarding to the ground truth.
{"title":"Gradient-Based Differential Approach for 3-D Motion Compensation in Interventional 2-D/3-D Image Fusion","authors":"Jian Wang, A. Borsdorf, B. Heigl, T. Köhler, J. Hornegger","doi":"10.1109/3DV.2014.45","DOIUrl":"https://doi.org/10.1109/3DV.2014.45","url":null,"abstract":"In interventional radiology, preoperative 3-D volumes can be fused with intra-operative 2-D fluoroscopic images. Since the accuracy is crucial to the clinical usability of image fusion, patient motion resulting in misalignments has to be corrected during the procedure. In this paper, a novel gradient based differential approach is proposed to estimate the 3-D rigid motion from the 2-D tracking of contour points. The mathematical relationship between the 3-D differential motion and the 2-D motion is derived using the 3-D gradient, based on which a tracking-based motion compensation pipeline is introduced. Given the initial registration, the contour points are extracted and tracked along 2-D frames. The 3-D rigid motion is estimated using the iteratively re-weighted least square minimization to enhance the robustness. Our novel approach is evaluated on 10 datasets consisting of 1010 monoplane fluoroscopic images of a thorax phantom with 3-D rigid motion. Over all datasets, the maximum structure shift in the 2-D projection caused by the 3-D motion varies from 17.3 mm to 33.2 mm. Our approach reduces the 2-D structure shift to the range of 1.93 mm to 6.52 mm. For the most challenging longitudinal off-plane rotation, our approach achieves an average coverage of 79.9% regarding to the ground truth.","PeriodicalId":275516,"journal":{"name":"2014 2nd International Conference on 3D Vision","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131425203","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}
Changil Kim, Ulrich Muller, H. Zimmer, Y. Pritch, A. Sorkine-Hornung, M. Gross
We address the problem of stereoscopic content generation from light fields using multi-perspective imaging. Our proposed method takes as input a light field and a target disparity map, and synthesizes a stereoscopic image pair by selecting light rays that fulfill the given target disparity constraints. We formulate this as a variational convex optimization problem. Compared to previous work, our method makes use of multi-view input to composite the new view with occlusions and disocclusions properly handled, does not require any correspondence information such as scene depth, is free from undesirable artifacts such as grid bias or image distortion, and is more efficiently solvable. In particular, our method is about ten times more memory efficient than the previous art, and is capable of processing higher resolution input. This is essential to make the proposed method practically applicable to realistic scenarios where HD content is standard. We demonstrate the effectiveness of our method experimentally.
{"title":"Memory Efficient Stereoscopy from Light Fields","authors":"Changil Kim, Ulrich Muller, H. Zimmer, Y. Pritch, A. Sorkine-Hornung, M. Gross","doi":"10.1109/3DV.2014.12","DOIUrl":"https://doi.org/10.1109/3DV.2014.12","url":null,"abstract":"We address the problem of stereoscopic content generation from light fields using multi-perspective imaging. Our proposed method takes as input a light field and a target disparity map, and synthesizes a stereoscopic image pair by selecting light rays that fulfill the given target disparity constraints. We formulate this as a variational convex optimization problem. Compared to previous work, our method makes use of multi-view input to composite the new view with occlusions and disocclusions properly handled, does not require any correspondence information such as scene depth, is free from undesirable artifacts such as grid bias or image distortion, and is more efficiently solvable. In particular, our method is about ten times more memory efficient than the previous art, and is capable of processing higher resolution input. This is essential to make the proposed method practically applicable to realistic scenarios where HD content is standard. We demonstrate the effectiveness of our method experimentally.","PeriodicalId":275516,"journal":{"name":"2014 2nd International Conference on 3D Vision","volume":"92 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123167209","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}
M. Slomp, Hiroshi Kawasaki, Furukawa Ryo, R. Sagawa
Range-based scanners built upon multiple cameras and projectors offer affordable, entire-shape and high-speed setups for 3D scanning. The point cloud streams produced by these devices require large amounts of storage space. Compressing these datasets is challenging since the capturing process may result in noise and surface irregularities, and consecutive frames can differ substantially in the overall point distribution. Exploiting spatial and temporal coherency is difficult on such conditions, but nonetheless crucial for achieving decent compression rates. This paper introduces a novel data structure, the temporal sparse voxel octree, capable of grouping spatio-temporal coherency of multiple point cloud streams into a single voxel hierarchy. In the data structure, a bit mask is attached to each node, existing nodes can then be reused at different frames by manipulating their bit masks, providing substantial memory savings. Although the technique yields some losses, the amount of loss can be controlled.
{"title":"Temporal Octrees for Compressing Dynamic Point Cloud Streams","authors":"M. Slomp, Hiroshi Kawasaki, Furukawa Ryo, R. Sagawa","doi":"10.1109/3DV.2014.79","DOIUrl":"https://doi.org/10.1109/3DV.2014.79","url":null,"abstract":"Range-based scanners built upon multiple cameras and projectors offer affordable, entire-shape and high-speed setups for 3D scanning. The point cloud streams produced by these devices require large amounts of storage space. Compressing these datasets is challenging since the capturing process may result in noise and surface irregularities, and consecutive frames can differ substantially in the overall point distribution. Exploiting spatial and temporal coherency is difficult on such conditions, but nonetheless crucial for achieving decent compression rates. This paper introduces a novel data structure, the temporal sparse voxel octree, capable of grouping spatio-temporal coherency of multiple point cloud streams into a single voxel hierarchy. In the data structure, a bit mask is attached to each node, existing nodes can then be reused at different frames by manipulating their bit masks, providing substantial memory savings. Although the technique yields some losses, the amount of loss can be controlled.","PeriodicalId":275516,"journal":{"name":"2014 2nd International Conference on 3D Vision","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114078591","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}
Advancements in computing power via Multi Core processors and GPUs have made large scale reconstruction modeling and real-time photorealistic rendering possible. However, in urban areas flat surfaces with little texture still challenge multiview algorithms. We present a method for planar area recognition and model correction while avoiding deformation of non-planar areas such as domes, pillars and plant matter. Our method works in object space, allows a global solution that is not affected by individual range map inaccuracies or poorly matched range maps. We describe a segmentation of the model into bounded planar and non-planar areas driven by a global error function incorporating model shape and original images texture. The error is minimized iteratively using locally restricted graph cuts and the model is corrected accordingly. The algorithm was run on various complex and challenging real-world urban scenes and synthetic photo-realistic images are created from novel viewpoints without noticeable deformities that are common to typical reconstructions.
{"title":"Piecewise Planar and Non-Planar Segmentation of Large Complex 3D Urban Models","authors":"A. Golbert, David Arnon, A. Sever","doi":"10.1109/3DV.2014.88","DOIUrl":"https://doi.org/10.1109/3DV.2014.88","url":null,"abstract":"Advancements in computing power via Multi Core processors and GPUs have made large scale reconstruction modeling and real-time photorealistic rendering possible. However, in urban areas flat surfaces with little texture still challenge multiview algorithms. We present a method for planar area recognition and model correction while avoiding deformation of non-planar areas such as domes, pillars and plant matter. Our method works in object space, allows a global solution that is not affected by individual range map inaccuracies or poorly matched range maps. We describe a segmentation of the model into bounded planar and non-planar areas driven by a global error function incorporating model shape and original images texture. The error is minimized iteratively using locally restricted graph cuts and the model is corrected accordingly. The algorithm was run on various complex and challenging real-world urban scenes and synthetic photo-realistic images are created from novel viewpoints without noticeable deformities that are common to typical reconstructions.","PeriodicalId":275516,"journal":{"name":"2014 2nd International Conference on 3D Vision","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116270360","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}
We propose an approach to enhance rough 3D geometry with fine details obtained from multiple normal maps. We begin with unaligned 2D normal maps and rough geometry, and automatically optimize the alignments through 2-step iterative registration algorithm. We then map the normals onto the surface, correcting and seamlessly blending them together. Finally, we optimize the geometry to produce high-quality 3D models that incorporate the high-frequency details from the normal maps. We demonstrate that our algorithm improves upon the results produced by some well-known algorithms: Poisson surface reconstruction [1] and the algorithm proposed by Nehab et al. [2].
{"title":"Merge2-3D: Combining Multiple Normal Maps with 3D Surfaces","authors":"Sema Berkiten, Xinyi Fan, S. Rusinkiewicz","doi":"10.1109/3DV.2014.22","DOIUrl":"https://doi.org/10.1109/3DV.2014.22","url":null,"abstract":"We propose an approach to enhance rough 3D geometry with fine details obtained from multiple normal maps. We begin with unaligned 2D normal maps and rough geometry, and automatically optimize the alignments through 2-step iterative registration algorithm. We then map the normals onto the surface, correcting and seamlessly blending them together. Finally, we optimize the geometry to produce high-quality 3D models that incorporate the high-frequency details from the normal maps. We demonstrate that our algorithm improves upon the results produced by some well-known algorithms: Poisson surface reconstruction [1] and the algorithm proposed by Nehab et al. [2].","PeriodicalId":275516,"journal":{"name":"2014 2nd International Conference on 3D Vision","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121365537","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}
Massimo Mauro, Hayko Riemenschneider, A. Signoroni, R. Leonardi, L. Gool
Multi-View Stereo (MVS) algorithms scale poorly on large image sets, and quickly become unfeasible to run on a single machine with limited memory. Typical solutions to lower the complexity include reducing the redundancy of the image set (view selection), and dividing the image set in groups to be processed independently (view clustering). A novel formulation for view selection is proposed here. We express the problem with an Integer Linear Programming (ILP) model, where cameras are modeled with binary variables, while the linear constraints enforce the completeness of the 3D reconstruction. The solution of the ILP leads to an optimal subset of selected cameras. As a second contribution, we integrate ILP camera selection with a view clustering approach which exploits Leveraged Affinity Propagation (LAP). LAP clustering can efficiently deal with large camera sets. We adapt the original algorithm so that it provides a set of overlapping clusters where the minimum and maximum sizes and the number of overlapping cameras can be specified. Evaluations on four different dataset show our solution provides significant complexity reductions and guarantees near-perfect coverage, making large reconstructions feasible even on a single machine.
{"title":"An Integer Linear Programming Model for View Selection on Overlapping Camera Clusters","authors":"Massimo Mauro, Hayko Riemenschneider, A. Signoroni, R. Leonardi, L. Gool","doi":"10.1109/3DV.2014.25","DOIUrl":"https://doi.org/10.1109/3DV.2014.25","url":null,"abstract":"Multi-View Stereo (MVS) algorithms scale poorly on large image sets, and quickly become unfeasible to run on a single machine with limited memory. Typical solutions to lower the complexity include reducing the redundancy of the image set (view selection), and dividing the image set in groups to be processed independently (view clustering). A novel formulation for view selection is proposed here. We express the problem with an Integer Linear Programming (ILP) model, where cameras are modeled with binary variables, while the linear constraints enforce the completeness of the 3D reconstruction. The solution of the ILP leads to an optimal subset of selected cameras. As a second contribution, we integrate ILP camera selection with a view clustering approach which exploits Leveraged Affinity Propagation (LAP). LAP clustering can efficiently deal with large camera sets. We adapt the original algorithm so that it provides a set of overlapping clusters where the minimum and maximum sizes and the number of overlapping cameras can be specified. Evaluations on four different dataset show our solution provides significant complexity reductions and guarantees near-perfect coverage, making large reconstructions feasible even on a single machine.","PeriodicalId":275516,"journal":{"name":"2014 2nd International Conference on 3D Vision","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122505491","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}
Shape recovery based on shading variations of a lighted object was recently revisited with improvements that allow for the photometric stereo approach to serve as a competitive alternative for other shape reconstruction methods. However, most efforts of using photometric stereo tend to ignore some factors that are relevant in practical applications. The approach we consider tackles the photometric stereo reconstruction in the case of near-field imaging which means that both camera and light sources are close to the imaged object. The known challenges that characterize the problem involve perspective viewing geometry, attenuation of light and possibly missing regions. Here, we pay special attention to the question of how to faithfully model these aspects and by the same token design an efficient and robust numerical solver. We present a well-posed mathematical representation that integrates the above assumptions into a single coherent model. The surface reconstruction in our near-field scenario can then be executed efficiently in linear time. The merging strategy of the irradiance equations provided for each light source allows us to consider a characteristic expansion model which enables the direct computation of the surface. We evaluate several types of light attenuation models with nonuniform albedo and noise on synthetic data using four virtual sources. We also demonstrate the proposed method on surface reconstruction of real data using three images, each one taken with a different light source.
{"title":"Close-Range Photometric Stereo with Point Light Sources","authors":"Aaron Wetzler, R. Kimmel, A. Bruckstein, R. Mecca","doi":"10.1109/3DV.2014.68","DOIUrl":"https://doi.org/10.1109/3DV.2014.68","url":null,"abstract":"Shape recovery based on shading variations of a lighted object was recently revisited with improvements that allow for the photometric stereo approach to serve as a competitive alternative for other shape reconstruction methods. However, most efforts of using photometric stereo tend to ignore some factors that are relevant in practical applications. The approach we consider tackles the photometric stereo reconstruction in the case of near-field imaging which means that both camera and light sources are close to the imaged object. The known challenges that characterize the problem involve perspective viewing geometry, attenuation of light and possibly missing regions. Here, we pay special attention to the question of how to faithfully model these aspects and by the same token design an efficient and robust numerical solver. We present a well-posed mathematical representation that integrates the above assumptions into a single coherent model. The surface reconstruction in our near-field scenario can then be executed efficiently in linear time. The merging strategy of the irradiance equations provided for each light source allows us to consider a characteristic expansion model which enables the direct computation of the surface. We evaluate several types of light attenuation models with nonuniform albedo and noise on synthetic data using four virtual sources. We also demonstrate the proposed method on surface reconstruction of real data using three images, each one taken with a different light source.","PeriodicalId":275516,"journal":{"name":"2014 2nd International Conference on 3D Vision","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114426859","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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