Learn More
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 [26]. 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(More)
—We study the problem of automatic recognition and segmentation of objects in indoor RGB-D scenes. We propose to formulate the object recognition and segmentation in RGB-D data as a binary object-background segmentation, using an informative set of features and grouping cues for small regular superpixels. The main novelty of the proposed approach is the(More)
—Future advancements in robot autonomy and sophistication of robotics tasks rest on robust, efficient, and task-dependent semantic understanding of the environment. Semantic segmentation is the problem of simultaneous segmentation and categorization of a partition of sensory data. The majority of current approaches tackle this using multi-class segmentation(More)
The fruit fly, Drosophila melanogaster, is a well established model organism used to study the mechanisms of both learning and memory in vivo. This paper presents video analysis algorithms that generate data that may be used to categorize fly behaviors. The algorithms aim to replace and improve a labor-intensive, subjective evaluation process with one that(More)
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(More)
  • 1