code: public Se3_F64 estimateOutliers( List<Point2D3D> observations ) {
  		// We can no longer trust that each point is a real observation.  Let's use RANSAC to separate the points
  		// You will need to tune the number of iterations and inlier threshold!!!
  		ModelMatcherMultiview<Se3_F64,Point2D3D> ransac =
  				FactoryMultiViewRobust.pnpRansac(new ConfigPnP(),new ConfigRansac(300,1.0));
  		ransac.setIntrinsic(0,intrinsic);
  
  		// Observations must be in normalized image coordinates!  See javadoc of pnpRansac
  		if( !ransac.process(observations) )
  			throw new RuntimeException("Probably got bad input data with NaN inside of it");
  
  		System.out.println("Inlier size "+ransac.getMatchSet().size());
  		Se3_F64 worldToCamera = ransac.getModelParameters();
  
  		// You will most likely want to refine this solution too.  Can make a difference with real world data
  		RefinePnP refine = FactoryMultiView.pnpRefine(1e-8,200);
  
  		Se3_F64 refinedWorldToCamera = new Se3_F64();
  
  		// notice that only the match set was passed in
  		if( !refine.fitModel(ransac.getMatchSet(),worldToCamera,refinedWorldToCamera) )
  			throw new RuntimeException("Refined failed! Input probably bad...");
  
  		return refinedWorldToCamera;
  	}
docs: Uses robust techniques to remove outliers