We present a new public dataset with a focus on simulating robotic vision
tasks in everyday indoor environments using real imagery. The dataset includes
20,000+ RGB-D images and 50,000+ 2D bounding boxes of object instances densely
captured in 9 unique scenes. We train a fast object category detector for
instance detection on our data. Using the dataset we show that, although
increasingly accurate and fast, the state of the art for object detection is
still severely impacted by object scale, occlusion, and viewing direction all
of which matter for robotics applications. We next validate the dataset for
simulating active vision, and use the dataset to develop and evaluate a
deep-network-based system for next best move prediction for object
classification using reinforcement learning. Our dataset is available for
download at cs.unc.edu/~ammirato/active_vision_dataset_website/.

该论文使用真实影像，聚焦日常室内环境下机器人视觉任务的模拟，构建新的公共数据集。该数据集可用于机器人视觉任务中的物体检测、主动视觉模拟和基于深度学习的强化学习下的下一步动作预测。在使用数据集的过程中，作者发现现有物体检测研究最大的问题是机器人视角对物体尺度，遮挡和方向的影响。