In order to accomplish the requirement of scene understanding to complete various kinds of complex tasks in home environment for robots,a novel instance segmentation method is adopted to build an instance-level 3d sem...
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In order to accomplish the requirement of scene understanding to complete various kinds of complex tasks in home environment for robots,a novel instance segmentation method is adopted to build an instance-level 3d semantic map and obtain information such as categories,positions and interrelationship of instance objects within the *** from the previous method which focuses on a certain feature in geometry or vision,we synchronously learn the features of geometric and visual information,distinguish instance objects and background areas and create the feature voxel grid of the *** proposed3d-RPN network takes the grid as input and makes use of the cuboid bounding box to predict each instance and the category it *** the mask prediction branch,we binarized voxels in each bounding box to determine the exact distribution of the instance obj *** method borrows the idea of Mask R-CNN and the main body is constructed by 3d and 2d convolutional network,making full use of the features of 2d and *** have tested our method on ScanNet and S3dIS,two large-scale indoor scene data sets,and the experiment has verified that our method can find and identify the instance information more accurately than previous methods.
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