This paper presents an end-to-end convolutional neural network (CNN) for
2D-3D exemplar detection. We demonstrate that the ability to adapt the features
of natural images to better align with those of CAD rendered views is critical
to the success of our technique...
We show that the adaptation can be learned by
compositing rendered views of textured object models on natural images. Our
approach can be naturally incorporated into a CNN detection pipeline and
extends the accuracy and speed benefits from recent advances in deep learning
to 2D-3D exemplar detection. We applied our method to two tasks: instance
detection, where we evaluated on the IKEA dataset, and object category
detection, where we out-perform Aubry et al. for "chair" detection on a subset
of the Pascal VOC dataset.