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.

本研究提出了一种用于检测 3D 物体的端到端深度学习系统，并可用于增强现实（AR）和机器人学的实时应用。该系统适用于许多语义类别，可以通过 2D 边界框来定位并同时定位其角点，有效生成盒状物体的 3D 解释，并通过迭代汇集卷积特征来优化改善其性能。