Published on Mon Feb 27 2017

A Dataset for Developing and Benchmarking Active Vision

Phil Ammirato, Patrick Poirson, Eunbyung Park, Jana Kosecka, Alexander C. Berg

The dataset includes 20,000+ RGB-D images and 50,000-2D bounding boxes of object instances densely captured in 9 unique scenes. The state of the art for object detection is still severely impacted by object scale, occlusion, and viewing direction.

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Abstract

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/.