  The goal of this work is to replace objects in an RGB-D scene with
corresponding 3D models from a library. We approach this problem by first
detecting and segmenting object instances in the scene using the approach from
Gupta et al. [13]. We use a convolutional neural network (CNN) to predict the
pose of the object. This CNN is trained using pixel normals in images
containing rendered synthetic objects. When tested on real data, it outperforms
alternative algorithms trained on real data. We then use this coarse pose
estimate along with the inferred pixel support to align a small number of
prototypical models to the data, and place the model that fits the best into
the scene. We observe a 48% relative improvement in performance at the task of
3D detection over the current state-of-the-art [33], while being an order of
magnitude faster at the same time.
