Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation
- 4 May 2010
- journal article
- research article
- Published by SAGE Publications in The International Journal of Robotics Research
- Vol. 29 (8), 1019-1037
- https://doi.org/10.1177/0278364910369190
Abstract
In recent years, object detection has become an increasingly active field of research in robotics. An important problem in object detection is the availability of a sufficient amount of labeled training data to learn good classifiers. In this paper we show how to significantly reduce the need for manually labeled training data by leveraging data sets available on the World Wide Web. Specifically, we show how to use objects from Google’s 3D Warehouse to train an object detection system for 3D point clouds collected by robots navigating through both urban and indoor environments. In order to deal with the different characteristics of the web data and the real robot data, we additionally use a small set of labeled point clouds and perform domain adaptation. Our experiments demonstrate that additional data taken from the 3D Warehouse along with our domain adaptation greatly improves the classification accuracy on real-world environments.Keywords
This publication has 11 references indexed in Scilit:
- The WEKA data mining softwareACM SIGKDD Explorations Newsletter, 2009
- 80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene RecognitionIeee Transactions On Pattern Analysis and Machine Intelligence, 2008
- Robotic Grasping of Novel Objects using VisionThe International Journal of Robotics Research, 2008
- Scene completion using millions of photographsACM Transactions on Graphics, 2007
- Content-Based Retrieval of 3-D Objects Using Spin Image SignaturesIEEE Transactions on Multimedia, 2007
- Mean shift: a robust approach toward feature space analysisIeee Transactions On Pattern Analysis and Machine Intelligence, 2002
- Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)The Annals of Statistics, 2000
- Using spin images for efficient object recognition in cluttered 3D scenesIEEE Transactions on Pattern Analysis and Machine Intelligence, 1999
- Multitask LearningMachine Learning, 1997
- Random sample consensusCommunications of the ACM, 1981