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We present a method to infer 3D pose and shape of vehicles from a singleimage. To tackle this ill-posed problem, we optimize two-scale projectionconsistency between the generated 3D hypotheses and their 2Dpseudo-measurements. Specifically, we use a morphable wireframe model togenerate a fine-scaled representation of vehicle shape and pose. To reduce itssensitivity to 2D landmarks, we jointly model the 3D bounding box as a coarserepresentation which improves robustness. We also integrate three task priors,including unsupervised monocular depth, a ground plane constraint as well asvehicle shape priors, with forward projection errors into an overall energyfunction.

Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors