Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Cuboid Detection: Beyond 2D Bounding Boxes   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.

11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin