We introduce a novel algorithm that decomposes a deformable shape into meaningful parts requiring... more We introduce a novel algorithm that decomposes a deformable shape into meaningful parts requiring only a single input pose. Using modal analysis, we are able to identify parts of the shape that tend to move rigidly. We define a deformation energy on the shape, enabling modal analysis to find the typical deformations of the shape. We then find a decomposition of the shape such that the typical deformations can be well approximated with deformation fields that are rigid in each part of the decomposition. We optimize for the best decomposition, which captures how the shape deforms. A hierarchical refinement scheme makes it possible to compute more detailed decompositions for some parts of the shape. Although our algorithm does not require user intervention, it is possible to control the process by directly changing the deformation energy, or interactively refining the decomposition as necessary. Due to the construction of the energy function and the properties of modal analysis, the computed decompositions are robust to changes in pose as well as meshing, noise, and even imperfections such as small holes in the surface.
We present an approach to segmenting shapes in a heterogenous shape database. Our approach segmen... more 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.
Simultaneously segmenting and labeling images is a fundamental problem in Computer Vision. In thi... more Simultaneously segmenting and labeling images is a fundamental problem in Computer Vision. In this paper, we introduce a hierarchical CRF model to deal with the problem of labeling images of street scenes by several distinctive object classes. In addition to learning a CRF model from all the labeled images, we group images into clusters of similar images and learn a CRF model from each cluster separately. When labeling a new image, we pick the closest cluster and use the associated CRF model to label this image. Experimental results show that this hierarchical image labeling method is comparable to, and in many cases superior to, previous methods on benchmark data sets. In addition to segmentation and labeling results, we also showed how to apply the image labeling result to rerank Google similar images.
: Geodesic patterns on freeform surfaces. Left: For the cladding of a surface by wooden panels be... more : Geodesic patterns on freeform surfaces. Left: For the cladding of a surface by wooden panels bendable only about their weak axis, we perform segmentation into parts which can be covered by geodesic strips of roughly constant width. Right: A timber construction derived from a hexagonal geodesic web has good manufacturing and statics properties.
In this paper, we describe a system for the reconstruction of deforming geometry from a time sequ... more In this paper, we describe a system for the reconstruction of deforming geometry from a time sequence of unstructured, noisy point clouds, as produced by recent real-time range scanning devices. Our technique reconstructs both the geometry and dense correspondences over time.
We introduce a novel algorithm that decomposes a deformable shape into meaningful parts requiring... more We introduce a novel algorithm that decomposes a deformable shape into meaningful parts requiring only a single input pose. Using modal analysis, we are able to identify parts of the shape that tend to move rigidly. We define a deformation energy on the shape, enabling modal analysis to find the typical deformations of the shape. We then find a decomposition of the shape such that the typical deformations can be well approximated with deformation fields that are rigid in each part of the decomposition. We optimize for the best decomposition, which captures how the shape deforms. A hierarchical refinement scheme makes it possible to compute more detailed decompositions for some parts of the shape. Although our algorithm does not require user intervention, it is possible to control the process by directly changing the deformation energy, or interactively refining the decomposition as necessary. Due to the construction of the energy function and the properties of modal analysis, the computed decompositions are robust to changes in pose as well as meshing, noise, and even imperfections such as small holes in the surface.
We present an approach to segmenting shapes in a heterogenous shape database. Our approach segmen... more 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.
Simultaneously segmenting and labeling images is a fundamental problem in Computer Vision. In thi... more Simultaneously segmenting and labeling images is a fundamental problem in Computer Vision. In this paper, we introduce a hierarchical CRF model to deal with the problem of labeling images of street scenes by several distinctive object classes. In addition to learning a CRF model from all the labeled images, we group images into clusters of similar images and learn a CRF model from each cluster separately. When labeling a new image, we pick the closest cluster and use the associated CRF model to label this image. Experimental results show that this hierarchical image labeling method is comparable to, and in many cases superior to, previous methods on benchmark data sets. In addition to segmentation and labeling results, we also showed how to apply the image labeling result to rerank Google similar images.
: Geodesic patterns on freeform surfaces. Left: For the cladding of a surface by wooden panels be... more : Geodesic patterns on freeform surfaces. Left: For the cladding of a surface by wooden panels bendable only about their weak axis, we perform segmentation into parts which can be covered by geodesic strips of roughly constant width. Right: A timber construction derived from a hexagonal geodesic web has good manufacturing and statics properties.
In this paper, we describe a system for the reconstruction of deforming geometry from a time sequ... more In this paper, we describe a system for the reconstruction of deforming geometry from a time sequence of unstructured, noisy point clouds, as produced by recent real-time range scanning devices. Our technique reconstructs both the geometry and dense correspondences over time.
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Papers by Qixing Huang