Quality assessment of monocular 3D inference

Recently proliferation of 3D inference methods shows an important alternative to perceive in 3D of real world from single images. The quality evaluation of 3D estimated from inference methods has been demonstrated using dataset with 3D ground truth data. However in real scenarios, the 3D inference quality is complete unknown. In this work, we present a new quality assessment of 3D monocular inference. First, we define the notion of quality index for 3D inference data. Then, we present a weighted linear model of similarity metrics to estimate quality index. The method is based on hand crafted similarity measures among image representations of RGB image and 3D inferred data. We demonstrate the effectiveness of our proposed method using public datasets and 3D inference methods of state of the art.