Author proposes 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 simulation, and (3) high photorealism of the synthesized images is achieved by physically based rendering.

Author claims that the Faster R-CNN model trained on images synthesized by the proposed approach, [1] achieves a 24% absolute improvement of [email protected] on Rutgers APC [2] and 11% on LineMod-Occluded [3] 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.

Author has also released a dataset of 400K synthetic images with each object instance annotated with a 2D bounding box, segmentation mask and a 6D pose.