Data-Driven Scene Understanding from 3D Models

author: Scott Satkin, The Robotics Institute, School of Computer Science, Carnegie Mellon University
published: Oct. 9, 2012,   recorded: September 2012,   views: 385
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Description

In this paper, we propose a data-driven approach to leverage repositories of 3D models for scene understanding. Our ability to relate what we see in an image to a large collection of 3D models allows us to transfer information from these models, creating a rich understanding of the scene. We develop a framework for auto-calibrating a camera, rendering 3D models from the viewpoint an image was taken, and computing a similarity measure between each 3D model and an input image. We demonstrate this data-driven approach in the context of geometry estimation and show the ability to find the identities and poses of object in a scene. Additionally, we present a new dataset with annotated scene geometry. This data allows us to measure the performance of our algorithm in 3D, rather than in the image plane.

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Download slides icon Download slides: bmvc2012_satkin_scene_understanding_01.pdf (24.3┬áMB)


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