Inventory of 3D Point Cloud Segments and 3D Point Modeling using RGB-D Camera


Inventory of 3D Point Cloud Segments and 3D Point Modeling using RGB-D Camera – A new computer vision tool called 3D-D Foreground Search (3D) has been developed to assist users in managing complex cluttered and clutter-laden objects. The key to this tool is to discover the 3D feature representation of clutter based on 2D point estimates of the surrounding objects and a 3D point model of the objects. Based on the 3D feature representation, 2D model of clutter is identified in a grid of various sizes, and a 3D model of clutter is considered by the user. The user can then create clutter objects and perform the search to locate those objects. The 3D feature representation and the clutter object knowledge are retrieved using a hierarchical system.

The problem of computing a local similarity between two data points is to learn a sparse representation for them and a global distribution with the same rank. In this paper, we propose a model for the problem of joint ranking, where a node must rank, and a local distribution can be computed. We show that this model can approximate the global distribution efficiently (using the rank component) and the ranking over a sample is the optimal estimation of the rank function in terms of the relative rank of the data points. We also show that this model is a generalization of sparse and additive clustering. Experimental results on the MNIST and CIFAR10 datasets, showing that the proposed model is very competitive with the state-of-the-art performance in terms of rank estimation and ranking.

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Inventory of 3D Point Cloud Segments and 3D Point Modeling using RGB-D Camera

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  • Learning Spatial-Temporal Features with Dense Neural Networks

    Multiple adaptive clustering by anisotropic diffusionThe problem of computing a local similarity between two data points is to learn a sparse representation for them and a global distribution with the same rank. In this paper, we propose a model for the problem of joint ranking, where a node must rank, and a local distribution can be computed. We show that this model can approximate the global distribution efficiently (using the rank component) and the ranking over a sample is the optimal estimation of the rank function in terms of the relative rank of the data points. We also show that this model is a generalization of sparse and additive clustering. Experimental results on the MNIST and CIFAR10 datasets, showing that the proposed model is very competitive with the state-of-the-art performance in terms of rank estimation and ranking.


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