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In this paper, we present a novel global descriptor M2DP for 3D point clouds, and apply it to the problem of loop closure detection. In M2DP, we project a 3D point cloud to multiple 2D planes and generate a density signature for points for each of the planes. We then use the left and right singular vectors of these signatures as the descriptor of the 3D point cloud. Our experimental results show that...
Objects in fine-grained categories always share a high degree of shape similarity, making both “localizing discriminative parts” and “learning appearance descriptors” extremely difficult. We propose a framework to leverage 2D+3D cues to handle above two challenges. Towards the goal of image alignment to localize discriminative parts, traditional methods rely on either manual part annotation or image...
In this paper, we investigate a novel reconfigurable part-based model, namely And-Or graph model, to recognize object shapes in images. Our proposed model consists of four layers: leaf-nodes at the bottom are local classifiers for detecting contour fragments; or-nodes above the leaf-nodes function as the switches to activate their child leaf-nodes, making the model reconfigurable during inference;...
Gender classification of depth images is a challenging problem, most research work attempted to use shape information to solve this problem in the past literature. In this work, we propose a new fusion scheme for gender classification using both texture and shape features. A new ensemble scheme is advocated to combine texture and shape feature at the feature level. To evaluate the performance of our...
This paper proposes a simple yet effective method to learn the hierarchical object shape model consisting of local contour fragments, which represents a category of shapes in the form of an And-Or tree. This model extends the traditional hierarchical tree structures by introducing the “switch” variables (i.e. the or-nodes) that explicitly specify production rules to capture shape variations. We thus...
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