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We propose a new method for the propagation of semantic labels in RGB-D video of indoor scenes given a set of ground truth keyframes. Manual labeling of all pixels in every frame of a video sequence is labor intensive and costly, yet required for training and testing of semantic segmentation methods. The availability of video enables propagation of labels between the frames for obtaining a large amounts...
We propose a novel approach for multi-view object detection in 3D scenes reconstructed from RGB-D sensor. We utilize shape based representation using local shape context descriptors along with the voting strategy which is supported by unsupervised object proposals generated from 3D point cloud data. Our algorithm starts with a single-view object detection where object proposals generated in 3D space...
This paper presents a new multi-view RGB-D dataset of nine kitchen scenes, each containing several objects in realistic cluttered environments including a subset of objects from the BigBird dataset. The viewpoints of the scenes are densely sampled and objects in the scenes are annotated with bounding boxes and in the 3D point cloud. Also, an approach for detection and recognition is presented, which...
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