We propose a transductive Gaussian process (TGP) regression method with regularized Laplacian kernels. Transductive learning exploits not only the labeled data but also the unlabeled test instances for learning. GPs are Bayesian probabilistic regressors which use only labeled data. To use unlabeled data in GPs, regularized Laplacian kernels are used. Similar to the case of a supervised GP regression, the proposed method provides not only the predicted target values but also their error bars. It also provides a hyperparameter selection method based on a Bayesian model selection scheme. We applied the proposed TGP method to the object pose estimation data sets as well as artificial data sets and compared the existing methods. Experimental results show that the proposed method has some advantages over the existing methods.