/Data-Driven Scene Understanding from 3D Models

Data-Driven Scene Understanding from 3D Models

Scott Satkin, Jason Lin and Martial Hebert
Conference Paper, Proceedings of the 23rd British Machine Vision Conference (BMVC), September, 2012

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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.

BibTeX Reference
author = {Scott Satkin and Jason Lin and Martial Hebert},
title = {Data-Driven Scene Understanding from 3D Models},
booktitle = {Proceedings of the 23rd British Machine Vision Conference (BMVC)},
year = {2012},
month = {September},