Jawadul H. Bappy, A. Roy-Chowdhury, Jason Bunk, L. Nataraj, B. S. Manjunath
The advent of high-tech journaling tools facilitates an image to be manipulated in a way that can easily evade state-of-the-art image tampering detection approaches. The recent success of the deep learning approaches in different recognition tasks inspires us to develop a high confidence detection framework which can localize manipulated regions in an image. Unlike semantic object segmentation where all meaningful regions (objects) are segmented, the localization of image manipulation focuses only the possible tampered region which makes the problem even more challenging. In order to formulate the framework, we employ a hybrid CNN-LSTM model to capture discriminative features between manipulated and non-manipulated regions. One of the key properties of manipulated regions is that they exhibit discriminative features in boundaries shared with neighboring non-manipulated pixels. Our motivation is to learn the boundary discrepancy, i.e., the spatial structure, between manipulated and non-manipulated regions with the combination of LSTM and convolution layers. We perform end-to-end training of the network to learn the parameters through back-propagation given ground-truth mask information. The overall framework is capable of detecting different types of image manipulations, including copy-move, removal and splicing. Our model shows promising results in localizing manipulated regions, which is demonstrated through rigorous experimentation on three diverse datasets.
{"title":"Exploiting Spatial Structure for Localizing Manipulated Image Regions","authors":"Jawadul H. Bappy, A. Roy-Chowdhury, Jason Bunk, L. Nataraj, B. S. Manjunath","doi":"10.1109/ICCV.2017.532","DOIUrl":"https://doi.org/10.1109/ICCV.2017.532","url":null,"abstract":"The advent of high-tech journaling tools facilitates an image to be manipulated in a way that can easily evade state-of-the-art image tampering detection approaches. The recent success of the deep learning approaches in different recognition tasks inspires us to develop a high confidence detection framework which can localize manipulated regions in an image. Unlike semantic object segmentation where all meaningful regions (objects) are segmented, the localization of image manipulation focuses only the possible tampered region which makes the problem even more challenging. In order to formulate the framework, we employ a hybrid CNN-LSTM model to capture discriminative features between manipulated and non-manipulated regions. One of the key properties of manipulated regions is that they exhibit discriminative features in boundaries shared with neighboring non-manipulated pixels. Our motivation is to learn the boundary discrepancy, i.e., the spatial structure, between manipulated and non-manipulated regions with the combination of LSTM and convolution layers. We perform end-to-end training of the network to learn the parameters through back-propagation given ground-truth mask information. The overall framework is capable of detecting different types of image manipulations, including copy-move, removal and splicing. Our model shows promising results in localizing manipulated regions, which is demonstrated through rigorous experimentation on three diverse datasets.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"101 1","pages":"4980-4989"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87005583","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}
Shengfeng He, Jianbo Jiao, Xiaodan Zhang, Guoqiang Han, Rynson W. H. Lau
Subitizing (i.e., instant judgement on the number) and detection of salient objects are human inborn abilities. These two tasks influence each other in the human visual system. In this paper, we delve into the complementarity of these two tasks. We propose a multi-task deep neural network with weight prediction for salient object detection, where the parameters of an adaptive weight layer are dynamically determined by an auxiliary subitizing network. The numerical representation of salient objects is therefore embedded into the spatial representation. The proposed joint network can be trained end-to-end using backpropagation. Experiments show the proposed multi-task network outperforms existing multi-task architectures, and the auxiliary subitizing network provides strong guidance to salient object detection by reducing false positives and producing coherent saliency maps. Moreover, the proposed method is an unconstrained method able to handle images with/without salient objects. Finally, we show state-of-theart performance on different salient object datasets.
{"title":"Delving into Salient Object Subitizing and Detection","authors":"Shengfeng He, Jianbo Jiao, Xiaodan Zhang, Guoqiang Han, Rynson W. H. Lau","doi":"10.1109/ICCV.2017.120","DOIUrl":"https://doi.org/10.1109/ICCV.2017.120","url":null,"abstract":"Subitizing (i.e., instant judgement on the number) and detection of salient objects are human inborn abilities. These two tasks influence each other in the human visual system. In this paper, we delve into the complementarity of these two tasks. We propose a multi-task deep neural network with weight prediction for salient object detection, where the parameters of an adaptive weight layer are dynamically determined by an auxiliary subitizing network. The numerical representation of salient objects is therefore embedded into the spatial representation. The proposed joint network can be trained end-to-end using backpropagation. Experiments show the proposed multi-task network outperforms existing multi-task architectures, and the auxiliary subitizing network provides strong guidance to salient object detection by reducing false positives and producing coherent saliency maps. Moreover, the proposed method is an unconstrained method able to handle images with/without salient objects. Finally, we show state-of-theart performance on different salient object datasets.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"5 1","pages":"1059-1067"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91115782","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}
In this paper, we present a technique that places 3D bounding boxes around objects in an RGB-D scene. Our approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-the-art 2D object detection techniques. We then use the 3D information to orient, place, and score bounding boxes around objects. We independently estimate the orientation for every object, using previous techniques that utilize normal information. Object locations and sizes in 3D are learned using a multilayer perceptron (MLP). In the final step, we refine our detections based on object class relations within a scene. When compared to state-of-the-art detection methods that operate almost entirely in the sparse 3D domain, extensive experiments on the well-known SUN RGB-D dataset [29] show that our proposed method is much faster (4.1s per image) in detecting 3D objects in RGB-D images and performs better (3 mAP higher) than the state-of-the-art method that is 4.7 times slower and comparably to the method that is two orders of magnitude slower. This work hints at the idea that 2D-driven object detection in 3D should be further explored, especially in cases where the 3D input is sparse.
{"title":"2D-Driven 3D Object Detection in RGB-D Images","authors":"Jean Lahoud, Bernard Ghanem","doi":"10.1109/ICCV.2017.495","DOIUrl":"https://doi.org/10.1109/ICCV.2017.495","url":null,"abstract":"In this paper, we present a technique that places 3D bounding boxes around objects in an RGB-D scene. Our approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-the-art 2D object detection techniques. We then use the 3D information to orient, place, and score bounding boxes around objects. We independently estimate the orientation for every object, using previous techniques that utilize normal information. Object locations and sizes in 3D are learned using a multilayer perceptron (MLP). In the final step, we refine our detections based on object class relations within a scene. When compared to state-of-the-art detection methods that operate almost entirely in the sparse 3D domain, extensive experiments on the well-known SUN RGB-D dataset [29] show that our proposed method is much faster (4.1s per image) in detecting 3D objects in RGB-D images and performs better (3 mAP higher) than the state-of-the-art method that is 4.7 times slower and comparably to the method that is two orders of magnitude slower. This work hints at the idea that 2D-driven object detection in 3D should be further explored, especially in cases where the 3D input is sparse.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"12 1","pages":"4632-4640"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73220590","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}
Detecting logo frequency and duration in sports videos provides sponsors an effective way to evaluate their advertising efforts. However, general-purposed object detection methods cannot address all the challenges in sports videos. In this paper, we propose a mutual-enhanced approach that can improve the detection of a logo through the information obtained from other simultaneously occurred logos. In a Fast-RCNN-based framework, we first introduce a homogeneity-enhanced re-ranking method by analyzing the characteristics of homogeneous logos in each frame, including type repetition, color consistency, and mutual exclusion. Different from conventional enhance mechanism that improves the weak proposals with the dominant proposals, our mutual method can also enhance the relatively significant proposals with weak proposals. Mutual enhancement is also included in our frame propagation mechanism that improves logo detection by utilizing the continuity of logos across frames. We use a tennis video dataset and an associated logo collection for detection evaluation. Experiments show that the proposed method outperforms existing methods with a higher accuracy.
{"title":"Mutual Enhancement for Detection of Multiple Logos in Sports Videos","authors":"Yuan Liao, Xiaoqing Lu, Chengcui Zhang, Yongtao Wang, Zhi Tang","doi":"10.1109/ICCV.2017.519","DOIUrl":"https://doi.org/10.1109/ICCV.2017.519","url":null,"abstract":"Detecting logo frequency and duration in sports videos provides sponsors an effective way to evaluate their advertising efforts. However, general-purposed object detection methods cannot address all the challenges in sports videos. In this paper, we propose a mutual-enhanced approach that can improve the detection of a logo through the information obtained from other simultaneously occurred logos. In a Fast-RCNN-based framework, we first introduce a homogeneity-enhanced re-ranking method by analyzing the characteristics of homogeneous logos in each frame, including type repetition, color consistency, and mutual exclusion. Different from conventional enhance mechanism that improves the weak proposals with the dominant proposals, our mutual method can also enhance the relatively significant proposals with weak proposals. Mutual enhancement is also included in our frame propagation mechanism that improves logo detection by utilizing the continuity of logos across frames. We use a tennis video dataset and an associated logo collection for detection evaluation. Experiments show that the proposed method outperforms existing methods with a higher accuracy.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"25 1","pages":"4856-4865"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73657557","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}
Transferring objects from one place to another place is a common task performed by human in daily life. During this process, it is usually intuitive for humans to choose an object as a proper container and to use an efficient pose to carry objects; yet, it is non-trivial for current computer vision and machine learning algorithms. In this paper, we propose an approach to jointly infer container and human pose for transferring objects by minimizing the costs associated both object and pose candidates. Our approach predicts which object to choose as a container while reasoning about how humans interact with physical surroundings to accomplish the task of transferring objects given visual input. In the learning phase, the presented method learns how humans make rational choices of containers and poses for transferring different objects, as well as the physical quantities required by the transfer task (e.g., compatibility between container and containee, energy cost of carrying pose) via a structured learning approach. In the inference phase, given a scanned 3D scene with different object candidates and a dictionary of human poses, our approach infers the best object as a container together with human pose for transferring a given object.
{"title":"Transferring Objects: Joint Inference of Container and Human Pose","authors":"Hanqing Wang, Wei Liang, L. Yu","doi":"10.1109/ICCV.2017.319","DOIUrl":"https://doi.org/10.1109/ICCV.2017.319","url":null,"abstract":"Transferring objects from one place to another place is a common task performed by human in daily life. During this process, it is usually intuitive for humans to choose an object as a proper container and to use an efficient pose to carry objects; yet, it is non-trivial for current computer vision and machine learning algorithms. In this paper, we propose an approach to jointly infer container and human pose for transferring objects by minimizing the costs associated both object and pose candidates. Our approach predicts which object to choose as a container while reasoning about how humans interact with physical surroundings to accomplish the task of transferring objects given visual input. In the learning phase, the presented method learns how humans make rational choices of containers and poses for transferring different objects, as well as the physical quantities required by the transfer task (e.g., compatibility between container and containee, energy cost of carrying pose) via a structured learning approach. In the inference phase, given a scanned 3D scene with different object candidates and a dictionary of human poses, our approach infers the best object as a container together with human pose for transferring a given object.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"156 1","pages":"2952-2960"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77483028","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}
To compress large datasets of high-dimensional descriptors, modern quantization schemes learn multiple codebooks and then represent individual descriptors as combinations of codewords. Once the codebooks are learned, these schemes encode descriptors independently. In contrast to that, we present a new coding scheme that arranges dataset descriptors into a set of arborescence graphs, and then encodes non-root descriptors by quantizing their displacements with respect to their parent nodes. By optimizing the structure of arborescences, our coding scheme can decrease the quantization error considerably, while incurring only minimal overhead on the memory footprint and the speed of nearest neighbor search in the compressed dataset compared to the independent quantization. The advantage of the proposed scheme is demonstrated in a series of experiments with datasets of SIFT and deep descriptors.
{"title":"AnnArbor: Approximate Nearest Neighbors Using Arborescence Coding","authors":"Artem Babenko, V. Lempitsky","doi":"10.1109/ICCV.2017.523","DOIUrl":"https://doi.org/10.1109/ICCV.2017.523","url":null,"abstract":"To compress large datasets of high-dimensional descriptors, modern quantization schemes learn multiple codebooks and then represent individual descriptors as combinations of codewords. Once the codebooks are learned, these schemes encode descriptors independently. In contrast to that, we present a new coding scheme that arranges dataset descriptors into a set of arborescence graphs, and then encodes non-root descriptors by quantizing their displacements with respect to their parent nodes. By optimizing the structure of arborescences, our coding scheme can decrease the quantization error considerably, while incurring only minimal overhead on the memory footprint and the speed of nearest neighbor search in the compressed dataset compared to the independent quantization. The advantage of the proposed scheme is demonstrated in a series of experiments with datasets of SIFT and deep descriptors.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"121 1","pages":"4895-4903"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76689321","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}
Detecting pedestrians that are partially occluded remains a challenging problem due to variations and uncertainties of partial occlusion patterns. Following a commonly used framework of handling partial occlusions by part detection, we propose a multi-label learning approach to jointly learn part detectors to capture partial occlusion patterns. The part detectors share a set of decision trees via boosting to exploit part correlations and also reduce the computational cost of applying these part detectors. The learned decision trees capture the overall distribution of all the parts. When used as a pedestrian detector individually, our part detectors learned jointly show better performance than their counterparts learned separately in different occlusion situations. The learned part detectors can be further integrated to better detect partially occluded pedestrians. Experiments on the Caltech dataset show state-of-the-art performance of our approach for detecting heavily occluded pedestrians.
{"title":"Multi-label Learning of Part Detectors for Heavily Occluded Pedestrian Detection","authors":"Chunluan Zhou, Junsong Yuan","doi":"10.1109/ICCV.2017.377","DOIUrl":"https://doi.org/10.1109/ICCV.2017.377","url":null,"abstract":"Detecting pedestrians that are partially occluded remains a challenging problem due to variations and uncertainties of partial occlusion patterns. Following a commonly used framework of handling partial occlusions by part detection, we propose a multi-label learning approach to jointly learn part detectors to capture partial occlusion patterns. The part detectors share a set of decision trees via boosting to exploit part correlations and also reduce the computational cost of applying these part detectors. The learned decision trees capture the overall distribution of all the parts. When used as a pedestrian detector individually, our part detectors learned jointly show better performance than their counterparts learned separately in different occlusion situations. The learned part detectors can be further integrated to better detect partially occluded pedestrians. Experiments on the Caltech dataset show state-of-the-art performance of our approach for detecting heavily occluded pedestrians.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"177 1","pages":"3506-3515"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77369814","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 addresses the problem of line pattern noise removal from a single image, such as rain streak, hyperspectral stripe and so on. Most of the previous methods model the line pattern noise in original image domain, which fail to explicitly exploit the directional characteristic, thus resulting in a redundant subspace with poor representation ability for those line pattern noise. To achieve a compact subspace for the line pattern structure, in this work, we incorporate a transformation into the image decomposition model so that maps the input image to a domain where the line pattern appearance has an extremely distinct low-rank structure, which naturally allows us to enforce a low-rank prior to extract the line pattern streak/stripe from the noisy image. Moreover, the random noise is usually mixed up with the line pattern noise, which makes the challenging problem much more difficult. While previous methods resort to the spectral or temporal correlation of the multi-images, we give a detailed analysis between the noisy and clean image in both local gradient and nonlocal domain, and propose a compositional directional total variational and low-rank prior for the image layer, thus to simultaneously accommodate both types of noise. The proposed method has been evaluated on two different tasks, including remote sensing image mixed random-stripe noise removal and rain streak removal, all of which obtain very impressive performances.
{"title":"Transformed Low-Rank Model for Line Pattern Noise Removal","authors":"Yi Chang, Luxin Yan, Sheng Zhong","doi":"10.1109/ICCV.2017.191","DOIUrl":"https://doi.org/10.1109/ICCV.2017.191","url":null,"abstract":"This paper addresses the problem of line pattern noise removal from a single image, such as rain streak, hyperspectral stripe and so on. Most of the previous methods model the line pattern noise in original image domain, which fail to explicitly exploit the directional characteristic, thus resulting in a redundant subspace with poor representation ability for those line pattern noise. To achieve a compact subspace for the line pattern structure, in this work, we incorporate a transformation into the image decomposition model so that maps the input image to a domain where the line pattern appearance has an extremely distinct low-rank structure, which naturally allows us to enforce a low-rank prior to extract the line pattern streak/stripe from the noisy image. Moreover, the random noise is usually mixed up with the line pattern noise, which makes the challenging problem much more difficult. While previous methods resort to the spectral or temporal correlation of the multi-images, we give a detailed analysis between the noisy and clean image in both local gradient and nonlocal domain, and propose a compositional directional total variational and low-rank prior for the image layer, thus to simultaneously accommodate both types of noise. The proposed method has been evaluated on two different tasks, including remote sensing image mixed random-stripe noise removal and rain streak removal, all of which obtain very impressive performances.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"649 1","pages":"1735-1743"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77670966","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}
Alessandro Penna, Sadegh Mohammadi, N. Jojic, Vittorio Murino
A popular approach to training classifiers of new image classes is to use lower levels of a pre-trained feed-forward neural network and retrain only the top. Thus, most layers simply serve as highly nonlinear feature extractors. While these features were found useful for classifying a variety of scenes and objects, previous work also demonstrated unusual levels of sensitivity to the input especially for images which are veering too far away from the training distribution. This can lead to surprising results as an imperceptible change in an image can be enough to completely change the predicted class. This occurs in particular in applications involving personal data, typically acquired with wearable cameras (e.g., visual lifelogs), where the problem is also made more complex by the dearth of new labeled training data that make supervised learning with deep models difficult. To alleviate these problems, in this paper we propose a new generative model that captures the feature distribution in new data. Its latent space then becomes more representative of the new data, while still retaining the generalization properties. In particular, we use constrained Markov walks over a counting grid for modeling image sequences, which not only yield good latent representations, but allow for excellent classification with only a handful of labeled training examples of the new scenes or objects, a scenario typical in lifelogging applications.
{"title":"Summarization and Classification of Wearable Camera Streams by Learning the Distributions over Deep Features of Out-of-Sample Image Sequences","authors":"Alessandro Penna, Sadegh Mohammadi, N. Jojic, Vittorio Murino","doi":"10.1109/ICCV.2017.464","DOIUrl":"https://doi.org/10.1109/ICCV.2017.464","url":null,"abstract":"A popular approach to training classifiers of new image classes is to use lower levels of a pre-trained feed-forward neural network and retrain only the top. Thus, most layers simply serve as highly nonlinear feature extractors. While these features were found useful for classifying a variety of scenes and objects, previous work also demonstrated unusual levels of sensitivity to the input especially for images which are veering too far away from the training distribution. This can lead to surprising results as an imperceptible change in an image can be enough to completely change the predicted class. This occurs in particular in applications involving personal data, typically acquired with wearable cameras (e.g., visual lifelogs), where the problem is also made more complex by the dearth of new labeled training data that make supervised learning with deep models difficult. To alleviate these problems, in this paper we propose a new generative model that captures the feature distribution in new data. Its latent space then becomes more representative of the new data, while still retaining the generalization properties. In particular, we use constrained Markov walks over a counting grid for modeling image sequences, which not only yield good latent representations, but allow for excellent classification with only a handful of labeled training examples of the new scenes or objects, a scenario typical in lifelogging applications.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"14 1","pages":"4336-4344"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81916672","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 a novel framework for reconstructing lightweight polygonal surfaces from point clouds. Unlike traditional methods that focus on either extracting good geometric primitives or obtaining proper arrangements of primitives, the emphasis of this work lies in intersecting the primitives (planes only) and seeking for an appropriate combination of them to obtain a manifold polygonal surface model without boundary.,,We show that reconstruction from point clouds can be cast as a binary labeling problem. Our method is based on a hypothesizing and selection strategy. We first generate a reasonably large set of face candidates by intersecting the extracted planar primitives. Then an optimal subset of the candidate faces is selected through optimization. Our optimization is based on a binary linear programming formulation under hard constraints that enforce the final polygonal surface model to be manifold and watertight. Experiments on point clouds from various sources demonstrate that our method can generate lightweight polygonal surface models of arbitrary piecewise planar objects. Besides, our method is capable of recovering sharp features and is robust to noise, outliers, and missing data.
{"title":"PolyFit: Polygonal Surface Reconstruction from Point Clouds","authors":"L. Nan, Peter Wonka","doi":"10.1109/ICCV.2017.258","DOIUrl":"https://doi.org/10.1109/ICCV.2017.258","url":null,"abstract":"We propose a novel framework for reconstructing lightweight polygonal surfaces from point clouds. Unlike traditional methods that focus on either extracting good geometric primitives or obtaining proper arrangements of primitives, the emphasis of this work lies in intersecting the primitives (planes only) and seeking for an appropriate combination of them to obtain a manifold polygonal surface model without boundary.,,We show that reconstruction from point clouds can be cast as a binary labeling problem. Our method is based on a hypothesizing and selection strategy. We first generate a reasonably large set of face candidates by intersecting the extracted planar primitives. Then an optimal subset of the candidate faces is selected through optimization. Our optimization is based on a binary linear programming formulation under hard constraints that enforce the final polygonal surface model to be manifold and watertight. Experiments on point clouds from various sources demonstrate that our method can generate lightweight polygonal surface models of arbitrary piecewise planar objects. Besides, our method is capable of recovering sharp features and is robust to noise, outliers, and missing data.","PeriodicalId":6559,"journal":{"name":"2017 IEEE International Conference on Computer Vision (ICCV)","volume":"34 1","pages":"2372-2380"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83117546","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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