  Detecting 3D objects from a single RGB image is intrinsically ambiguous, thus
requiring appropriate prior knowledge and intermediate representations as
constraints to reduce the uncertainties and improve the consistencies between
the 2D image plane and the 3D world coordinate. To address this challenge, we
propose to adopt perspective points as a new intermediate representation for 3D
object detection, defined as the 2D projections of local Manhattan 3D keypoints
to locate an object; these perspective points satisfy geometric constraints
imposed by the perspective projection. We further devise PerspectiveNet, an
end-to-end trainable model that simultaneously detects the 2D bounding box, 2D
perspective points, and 3D object bounding box for each object from a single
RGB image. PerspectiveNet yields three unique advantages: (i) 3D object
bounding boxes are estimated based on perspective points, bridging the gap
between 2D and 3D bounding boxes without the need of category-specific 3D shape
priors. (ii) It predicts the perspective points by a template-based method, and
a perspective loss is formulated to maintain the perspective constraints. (iii)
It maintains the consistency between the 2D perspective points and 3D bounding
boxes via a differentiable projective function. Experiments on SUN RGB-D
dataset show that the proposed method significantly outperforms existing
RGB-based approaches for 3D object detection.
