  We present an approach to synthesize highly photorealistic images of 3D
object models, which we use to train a convolutional neural network for
detecting the objects in real images. The proposed approach has three key
ingredients: (1) 3D object models are rendered in 3D models of complete scenes
with realistic materials and lighting, (2) plausible geometric configuration of
objects and cameras in a scene is generated using physics simulations, and (3)
high photorealism of the synthesized images achieved by physically based
rendering. When trained on images synthesized by the proposed approach, the
Faster R-CNN object detector achieves a 24% absolute improvement of<EMAIL_ADDRESS>on Rutgers APC and 11% on LineMod-Occluded datasets, compared to a baseline
where the training images are synthesized by rendering object models on top of
random photographs. This work is a step towards being able to effectively train
object detectors without capturing or annotating any real images. A dataset of
600K synthetic images with ground truth annotations for various computer vision
tasks will be released on the project website: thodan.github.io/objectsynth.
