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View-Volume Network for Semantic Scene Completion from a Single Depth Image
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
We introduce a View-Volume convolutional neural network (VVNet) for inferring the occupancy and semantic labels of a volumetric 3D scene from a single depth image. Our method extracts the detailed geometric features from the input depth image with a 2D view CNN and then projects the features into a 3D volume according to the input depth map via a projection layer. After that, we learn the 3D context information of the scene with a 3D volume CNN for computing the result volumetric occupancy and
doi:10.24963/ijcai.2018/101
dblp:conf/ijcai/GuoT18
fatcat:nqg6yb5bpfef7aquu3q4tpgljq