DEPT: Depth Estimation by Parameter Transfer for Single Still Images

In this paper, we propose a new method for automatic depth estimation from color images using parameter transfer. By modeling the correlation between color images and their depth maps with a set of parameters, we get a database of parameter sets. Given an input image, we compute the high-level features to find the best matched image sets from the database. Then the set of parameters corresponding to the best match are used to estimate the depth of the input image. Compared to the past learning-based methods, our trained database only consists of trained features and parameter sets, which occupy little space. We evaluate our depth estimation method on the benchmark RGB-D (RGB + depth) datasets. The experimental results are comparable to the stateof-the-art, demonstrating the promising performance of our proposed method.