{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multiview-rgb-d-dataset-for-object-instance","title":"Multiview RGB-D Dataset for Object Instance Detection","arxiv_id":"1609.07826","date":"2016-09-26","proceeding":null,"authors":["Georgios Georgakis","Md. Alimoor Reza","Arsalan Mousavian","Phi-Hung Le","Jana Kosecka"],"abstract":"This paper presents a new multi-view RGB-D dataset of nine kitchen scenes,\neach containing several objects in realistic cluttered environments including a\nsubset of objects from the BigBird dataset. The viewpoints of the scenes are\ndensely sampled and objects in the scenes are annotated with bounding boxes and\nin the 3D point cloud. Also, an approach for detection and recognition is\npresented, which is comprised of two parts: i) a new multi-view 3D proposal\ngeneration method and ii) the development of several recognition baselines\nusing AlexNet to score our proposals, which is trained either on crops of the\ndataset or on synthetically composited training images. Finally, we compare the\nperformance of the object proposals and a detection baseline to the Washington\nRGB-D Scenes (WRGB-D) dataset and demonstrate that our Kitchen scenes dataset\nis more challenging for object detection and recognition. The dataset is\navailable at: http://cs.gmu.edu/~robot/gmu-kitchens.html.","url_abs":"http://arxiv.org/abs/1609.07826v1","url_pdf":"http://arxiv.org/pdf/1609.07826v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"local-response-normalization","method_name":"Local Response Normalization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[{"slug":"kitchen-scenes","name":"Kitchen Scenes","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.07826","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}