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Joint 3D Estimation of Objects and Scene Layout
Andreas Geiger · Christian Wojek · Raquel Urtasun

Mon Dec 12 10:00 AM -- 02:59 PM (PST) @

We propose a novel generative model that is able to reason jointly about the 3D scene layout as well as the 3D location and orientation of objects in the scene. In particular, we infer the scene topology, geometry as well as traffic activities from a short video sequence acquired with a single camera mounted on a moving car. Our generative model takes advantage of dynamic information in the form of vehicle tracklets as well as static information coming from semantic labels and geometry (i.e., vanishing points). Experiments show that our approach outperforms a discriminative baseline based on multiple kernel learning (MKL) which has access to the same image information. Furthermore, as we reason about objects in 3D, we are able to significantly increase the performance of state-of-the-art object detectors in their ability to estimate object orientation.

Author Information

Andreas Geiger (KIT)
Christian Wojek
Raquel Urtasun (University of Toronto)

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