The Experts below are selected from a list of 44094 Experts worldwide ranked by ideXlab platform
Leonidas J Guibas - One of the best experts on this subject based on the ideXlab platform.
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adacoseg adaptive Shape co Segmentation with group consistency loss
Computer Vision and Pattern Recognition, 2020Co-Authors: Chenyang Zhu, Leonidas J Guibas, Siddhartha Chaudhuri, Hao ZhangAbstract:We introduce AdaCoSeg, a deep neural network architecture for adaptive co-Segmentation of a set of 3D Shapes represented as point clouds. Differently from the familiar single-instance Segmentation problem, co-Segmentation is intrinsically contextual: how a Shape is segmented can vary depending on the set it is in. Hence, our network features an adaptive learning module to produce a consistent Shape Segmentation which adapts to a set. Specifically, given an input set of unsegmented Shapes, we first employ an offline pre-trained part prior network to propose per-Shape parts. Then the co-Segmentation network iteratively and jointly optimizes the part labelings across the set subjected to a novel group consistency loss defined by matrix ranks. While the part prior network can be trained with noisy and inconsistently segmented Shapes, the final output of AdaSeg is a consistent part labeling for the input set, with each Shape segmented into up to (a user-specified) K parts. Overall, our method is weakly supervised, producing Segmentations tailored to the test set, without consistent ground-truth Segmentations. We show qualitative and quantitative results from AdaSeg and evaluate it via ablation studies and comparisons to state-of-the-art co-Segmentation methods.
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syncspeccnn synchronized spectral cnn for 3d Shape Segmentation
Computer Vision and Pattern Recognition, 2017Co-Authors: Xingwen Guo, Leonidas J GuibasAbstract:In this paper, we study the problem of semantic annotation on 3D models that are represented as Shape graphs. A functional view is taken to represent localized information on graphs, so that annotations such as part segment or keypoint are nothing but 0-1 indicator vertex functions. Compared with images that are 2D grids, Shape graphs are irregular and non-isomorphic data structures. To enable the prediction of vertex functions on them by convolutional neural networks, we resort to spectral CNN method that enables weight sharing by parametrizing kernels in the spectral domain spanned by graph Laplacian eigenbases. Under this setting, our network, named SyncSpecCNN, strives to overcome two key challenges: how to share coefficients and conduct multi-scale analysis in different parts of the graph for a single Shape, and how to share information across related but different Shapes that may be represented by very different graphs. Towards these goals, we introduce a spectral parametrization of dilated convolutional kernels and a spectral transformer network. Experimentally we tested SyncSpecCNN on various tasks, including 3D Shape part Segmentation and keypoint prediction. State-of-the-art performance has been achieved on all benchmark datasets.
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learning hierarchical Shape Segmentation and labeling from online repositories
arXiv: Graphics, 2017Co-Authors: Leonidas J Guibas, Aaron Hertzmann, Vladimir G Kim, Ersin YumerAbstract:We propose a method for converting geometric Shapes into hierarchically segmented parts with part labels. Our key idea is to train category-specific models from the scene graphs and part names that accompany 3D Shapes in public repositories. These freely-available annotations represent an enormous, untapped source of information on geometry. However, because the models and corresponding scene graphs are created by a wide range of modelers with different levels of expertise, modeling tools, and objectives, these models have very inconsistent Segmentations and hierarchies with sparse and noisy textual tags. Our method involves two analysis steps. First, we perform a joint optimization to simultaneously cluster and label parts in the database while also inferring a canonical tag dictionary and part hierarchy. We then use this labeled data to train a method for hierarchical Segmentation and labeling of new 3D Shapes. We demonstrate that our method can mine complex information, detecting hierarchies in man-made objects and their constituent parts, obtaining finer scale details than existing alternatives. We also show that, by performing domain transfer using a few supervised examples, our technique outperforms fully-supervised techniques that require hundreds of manually-labeled models.
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joint Shape Segmentation with linear programming
International Conference on Computer Graphics and Interactive Techniques, 2011Co-Authors: Qixing Huang, Vladlen Koltun, Leonidas J GuibasAbstract:We present an approach to segmenting Shapes in a heterogenous Shape database. Our approach segments the Shapes jointly, utilizing features from multiple Shapes to improve the Segmentation of each. The approach is entirely unsupervised and is based on an integer quadratic programming formulation of the joint Segmentation problem. The program optimizes over possible Segmentations of individual Shapes as well as over possible correspondences between segments from multiple Shapes. The integer quadratic program is solved via a linear programming relaxation, using a block coordinate descent procedure that makes the optimization feasible for large databases. We evaluate the presented approach on the Princeton Segmentation benchmark and show that joint Shape Segmentation significantly outperforms single-Shape Segmentation techniques.
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Shape Segmentation using local slippage analysis
Symposium on Geometry Processing, 2004Co-Authors: Natasha Gelfand, Leonidas J GuibasAbstract:We propose a method for Segmentation of 3D scanned Shapes into simple geometric parts. Given an input point cloud, our method computes a set of components which possess one or more slippable motions: rigid motions which, when applied to a Shape, slide the transformed version against the stationary version without forming any gaps. Slippable Shapes include rotationally and translationally symmetrical Shapes such as planes, spheres, and cylinders, which are often found as components of scanned mechanical parts. We show how to determine the slippable motions of a given Shape by computing eigenvalues of a certain symmetric matrix derived from the points and normals of the Shape. Our algorithm then discovers slippable components in the input data by computing local slippage signatures at a set of points of the input and iteratively aggregating regions with matching slippable motions. We demonstrate the performance of our algorithm for reverse engineering surfaces of mechanical parts.
Leonidas Guibas - One of the best experts on this subject based on the ideXlab platform.
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SyncSpecCNN: Synchronized spectral CNN for 3D Shape Segmentation
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition CVPR 2017, 2017Co-Authors: Li Yi, Xingwen Guo, Hao Su, Leonidas GuibasAbstract:In this paper, we study the problem of semantic annotation on 3D models that are represented as Shape graphs. A functional view is taken to represent localized information on graphs, so that annotations such as part segment or keypoint are nothing but 0-1 indicator vertex functions. Compared with images that are 2D grids, Shape graphs are irregular and non-isomorphic data structures. To enable the prediction of vertex functions on them by convolutional neural networks, we resort to spectral CNN method that enables weight sharing by parameterizing kernels in the spectral domain spanned by graph laplacian eigenbases. Under this setting, our network, named SyncSpecCNN, strive to overcome two key challenges: how to share coefficients and conduct multi-scale analysis in different parts of the graph for a single Shape, and how to share information across related but different Shapes that may be represented by very different graphs. Towards these goals, we introduce a spectral parameterization of dilated convolutional kernels and a spectral transformer network. Experimentally we tested our SyncSpecCNN on various tasks, including 3D Shape part Segmentation and 3D keypoint prediction. State-of-the-art performance has been achieved on all benchmark datasets.
Li Yi - One of the best experts on this subject based on the ideXlab platform.
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SyncSpecCNN: Synchronized spectral CNN for 3D Shape Segmentation
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition CVPR 2017, 2017Co-Authors: Li Yi, Xingwen Guo, Hao Su, Leonidas GuibasAbstract:In this paper, we study the problem of semantic annotation on 3D models that are represented as Shape graphs. A functional view is taken to represent localized information on graphs, so that annotations such as part segment or keypoint are nothing but 0-1 indicator vertex functions. Compared with images that are 2D grids, Shape graphs are irregular and non-isomorphic data structures. To enable the prediction of vertex functions on them by convolutional neural networks, we resort to spectral CNN method that enables weight sharing by parameterizing kernels in the spectral domain spanned by graph laplacian eigenbases. Under this setting, our network, named SyncSpecCNN, strive to overcome two key challenges: how to share coefficients and conduct multi-scale analysis in different parts of the graph for a single Shape, and how to share information across related but different Shapes that may be represented by very different graphs. Towards these goals, we introduce a spectral parameterization of dilated convolutional kernels and a spectral transformer network. Experimentally we tested our SyncSpecCNN on various tasks, including 3D Shape part Segmentation and 3D keypoint prediction. State-of-the-art performance has been achieved on all benchmark datasets.
Xingwen Guo - One of the best experts on this subject based on the ideXlab platform.
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syncspeccnn synchronized spectral cnn for 3d Shape Segmentation
Computer Vision and Pattern Recognition, 2017Co-Authors: Xingwen Guo, Leonidas J GuibasAbstract:In this paper, we study the problem of semantic annotation on 3D models that are represented as Shape graphs. A functional view is taken to represent localized information on graphs, so that annotations such as part segment or keypoint are nothing but 0-1 indicator vertex functions. Compared with images that are 2D grids, Shape graphs are irregular and non-isomorphic data structures. To enable the prediction of vertex functions on them by convolutional neural networks, we resort to spectral CNN method that enables weight sharing by parametrizing kernels in the spectral domain spanned by graph Laplacian eigenbases. Under this setting, our network, named SyncSpecCNN, strives to overcome two key challenges: how to share coefficients and conduct multi-scale analysis in different parts of the graph for a single Shape, and how to share information across related but different Shapes that may be represented by very different graphs. Towards these goals, we introduce a spectral parametrization of dilated convolutional kernels and a spectral transformer network. Experimentally we tested SyncSpecCNN on various tasks, including 3D Shape part Segmentation and keypoint prediction. State-of-the-art performance has been achieved on all benchmark datasets.
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SyncSpecCNN: Synchronized spectral CNN for 3D Shape Segmentation
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition CVPR 2017, 2017Co-Authors: Li Yi, Xingwen Guo, Hao Su, Leonidas GuibasAbstract:In this paper, we study the problem of semantic annotation on 3D models that are represented as Shape graphs. A functional view is taken to represent localized information on graphs, so that annotations such as part segment or keypoint are nothing but 0-1 indicator vertex functions. Compared with images that are 2D grids, Shape graphs are irregular and non-isomorphic data structures. To enable the prediction of vertex functions on them by convolutional neural networks, we resort to spectral CNN method that enables weight sharing by parameterizing kernels in the spectral domain spanned by graph laplacian eigenbases. Under this setting, our network, named SyncSpecCNN, strive to overcome two key challenges: how to share coefficients and conduct multi-scale analysis in different parts of the graph for a single Shape, and how to share information across related but different Shapes that may be represented by very different graphs. Towards these goals, we introduce a spectral parameterization of dilated convolutional kernels and a spectral transformer network. Experimentally we tested our SyncSpecCNN on various tasks, including 3D Shape part Segmentation and 3D keypoint prediction. State-of-the-art performance has been achieved on all benchmark datasets.
Oliver Van Kaick - One of the best experts on this subject based on the ideXlab platform.
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rpm net recurrent prediction of motion and parts from point cloud
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Zihao Yan, Oliver Van Kaick, Xingguang Yan, Luanmin Chen, Hao Zhang, Hui HuangAbstract:We introduce RPM-Net, a deep learning-based approach which simultaneously infers movable parts and hallucinates their motions from a single, un-segmented, and possibly partial, 3D point cloud Shape. RPM-Net is a novel Recurrent Neural Network (RNN), composed of an encoder-decoder pair with interleaved Long Short-Term Memory (LSTM) components, which together predict a temporal sequence of pointwise displacements for the input point cloud. At the same time, the displacements allow the network to learn movable parts, resulting in a motion-based Shape Segmentation. Recursive applications of RPM-Net on the obtained parts can predict finer-level part motions, resulting in a hierarchical object Segmentation. Furthermore, we develop a separate network to estimate part mobilities, e.g., per-part motion parameters, from the segmented motion sequence. Both networks learn deep predictive models from a training set that exemplifies a variety of mobilities for diverse objects. We show results of simultaneous motion and part predictions from synthetic and real scans of 3D objects exhibiting a variety of part mobilities, possibly involving multiple movable parts.
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Shape Segmentation by approximate convexity analysis
ACM Transactions on Graphics, 2014Co-Authors: Oliver Van Kaick, Noa Fish, Yanir Kleiman, Shmuel Asafi, Daniel CohenorAbstract:We present a Shape Segmentation method for complete and incomplete Shapes. The key idea is to directly optimize the decomposition based on a characterization of the expected geometry of a part in a Shape. Rather than setting the number of parts in advance, we search for the smallest number of parts that admit the geometric characterization of the parts. The Segmentation is based on an intermediate-level analysis, where first the Shape is decomposed into approximate convex components, which are then merged into consistent parts based on a nonlocal geometric signature. Our method is designed to handle incomplete Shapes, represented by point clouds. We show Segmentation results on Shapes acquired by a range scanner, and an analysis of the robustness of our method to missing regions. Moreover, our method yields results that are comparable to state-of-the-art techniques evaluated on complete Shapes.
