3D ShapeNets: A deep representation for volumetric shapes
Citations
9,457 citations
Cites background or methods from "3D ShapeNets: A deep representation..."
...Volumetric CNNs: [25, 15, 16] are the pioneers applying 3D convolutional neural networks on voxelized shapes....
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...We evaluate our model on the ModelNet40 [25] shape classification benchmark....
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...Multiview CNNs: [24, 19] have tried to render 3D point cloud or shapes into 2D images and then apply 2D conv nets to classify them....
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...Volumetric CNNs: [29, 18, 19] are the pioneers applying 3D convolutional neural networks on voxelized shapes....
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...We evaluate our model on the ModelNet40 [29] shape classification benchmark....
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4,802 citations
Cites methods from "3D ShapeNets: A deep representation..."
...Datasets We evaluate on four datasets ranging from 2D objects (MNIST [11]), 3D objects (ModelNet40 [31] rigid object, SHREC15 [12] non-rigid object) to real 3D scenes (ScanNet [5])....
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3,727 citations
3,707 citations
Cites background from "3D ShapeNets: A deep representation..."
...Scene understanding from 2D images is a grand challenge in vision that has recently benefited tremendously from 3D CAD models [28, 34]....
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...Recent work demonstrated the benefit of a large dataset of 120K 3D CAD models in training a convolutional neural network for object recognition and next-best view prediction in RGB-D data [34]....
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..., upright and front) for every model is important for various tasks such as visualizing shapes [13], shape classification [8] and shape recognition [34]....
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3,053 citations
References
73,978 citations
17,598 citations
15,055 citations
"3D ShapeNets: A deep representation..." refers background or methods in this paper
...To represent the probability distribution of these binary variables for 3D shapes, we design a Convolutional Deep Belief Network (CDBN)....
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...The great generative power of deep learning models has allowed researchers to build deep generative models for 2D shapes: most notably the DBN [15] to generate handwritten digits and ShapeBM [10] to generate horses, etc....
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...The top layer forms an associative memory DBN as indicated by the bi-directional arrows, while all the other layer connections are directed top-down....
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...After training the CDBN, the model learns the joint distribution p(x, y) of voxel data x and object category label y ∈ {1, · · · ,K}....
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...A fully connected DBN on such an image would result in a huge number of parameters making the model intractable to train effectively....
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10,501 citations
"3D ShapeNets: A deep representation..." refers background in this paper
...[11, 19]), mostly due to the lack of a good generic representation for 3D geometric shapes....
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5,464 citations
"3D ShapeNets: A deep representation..." refers background in this paper
...[5, 22]), the success of 3D-based methods has largely been limited to instance recognition (e....
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