  Monocular 3D object parsing is highly desirable in various scenarios
including occlusion reasoning and holistic scene interpretation. We present a
deep convolutional neural network (CNN) architecture to localize semantic parts
in 2D image and 3D space while inferring their visibility states, given a
single RGB image. Our key insight is to exploit domain knowledge to regularize
the network by deeply supervising its hidden layers, in order to sequentially
infer intermediate concepts associated with the final task. To acquire training
data in desired quantities with ground truth 3D shape and relevant concepts, we
render 3D object CAD models to generate large-scale synthetic data and simulate
challenging occlusion configurations between objects. We train the network only
on synthetic data and demonstrate state-of-the-art performances on real image
benchmarks including an extended version of KITTI, PASCAL VOC, PASCAL3D+ and
IKEA for 2D and 3D keypoint localization and instance segmentation. The
empirical results substantiate the utility of our deep supervision scheme by
demonstrating effective transfer of knowledge from synthetic data to real
images, resulting in less overfitting compared to standard end-to-end training.
