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 …

In this supplementary material, Section 2 details the 3D annotation for CAD models and real
images as well as our approach to compute the car yaw angle given a 3D skeleton. Next, in
Section 3, we present the instance segmentation algorithm in detail for our experiments on
PASCAL3D+. Finally, we provide more quantitative results on KITTI-3D in Section 4 and
demonstrate more qualitative results on KITTI-3D, PASCAL VOC and IKEA datasets in
Section 5.