We present a Deep Cuboid Detector which takes a consumer-quality RGB image of a cluttered scene and localizes all 3D cuboids (box-like objects). Contrary to classical approaches which fit a 3D model from low-level cues like corners, edges, and vanishing points, we propose an end-to-end deep learning system to detect cuboids across many semantic categories (e.g., ovens, shipping boxes, and furniture). We localize cuboids with a 2D bounding box, and simultaneously localize the cuboid's corners, effectively producing a 3D interpretation of box-like objects. We refine keypoints by pooling convolutional features iteratively, improving the baseline method significantly. Our deep learning cuboid detector is trained in an end-to-end fashion and is suitable for real-time applications in augmented reality (AR) and robotics.
@article{arxiv.1611.10010,
title = {Deep Cuboid Detection: Beyond 2D Bounding Boxes},
author = {Debidatta Dwibedi and Tomasz Malisiewicz and Vijay Badrinarayanan and Andrew Rabinovich},
journal= {arXiv preprint arXiv:1611.10010},
year = {2016}
}