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Semantic Structure from Motion: A Novel Framework for Joint Object Recognition and 3D Reconstruction

  • Sid Yingze Bao
  • Silvio Savarese
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7474)

Abstract

Conventional rigid structure from motion (SFM) addresses the problem of recovering the camera parameters (motion) and the 3D locations (structure) of scene points, given observed 2D image feature points. In this chapter, we propose a new formulation called Semantic Structure From Motion (SSFM). In addition to the geometrical constraints provided by SFM, SSFM takes advantage of both semantic and geometrical properties associated with objects in a scene. These properties allow to jointly estimate the structure of the scene, the camera parameters as well as the 3D locations, poses, and categories of objects in a scene. We cast this problem as a max-likelihood problem where geometry (cameras, points, objects) and semantic information (object classes) are simultaneously estimated. The key intuition is that, in addition to image features, the measurements of objects across views provide additional geometrical constraints that relate cameras and scene parameters. These constraints make the geometry estimation process more robust and, in turn, make object detection more accurate. Our framework has the unique ability to: i) estimate camera poses only from object detections, ii) enhance camera pose estimation, compared to feature-point-based SFM algorithms, iii) improve object detections given multiple uncalibrated images, compared to independently detecting objects in single images. Extensive quantitative results on three datasets – LiDAR cars, street-view pedestrians, and Kinect office desktop – verify our theoretical claims.

Keywords

Object Detection Camera Parameter Semantic Structure Bundle Adjustment Structure From Motion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sid Yingze Bao
    • 1
  • Silvio Savarese
    • 1
  1. 1.The University of MichiganAnn ArborUSA

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