Multiview RGB-D Dataset for Object Instance Detection
This work provides a dataset for object detection in cluttered kitchen environments, which is incremental as it builds upon existing datasets like BigBird.
The authors introduced a new multi-view RGB-D dataset of kitchen scenes with dense viewpoint sampling and object annotations, and presented a detection and recognition approach using multi-view 3D proposal generation and AlexNet-based baselines, showing that their dataset is more challenging than the Washington RGB-D Scenes dataset.
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 is comprised of two parts: i) a new multi-view 3D proposal generation method and ii) the development of several recognition baselines using AlexNet to score our proposals, which is trained either on crops of the dataset or on synthetically composited training images. Finally, we compare the performance of the object proposals and a detection baseline to the Washington RGB-D Scenes (WRGB-D) dataset and demonstrate that our Kitchen scenes dataset is more challenging for object detection and recognition. The dataset is available at: http://cs.gmu.edu/~robot/gmu-kitchens.html.