DFNet-Trans: An end-to-end multibranching network for depth estimation for transparent objects

Abstract

Transparent objects play a vital role in modern industries and find widespread applications across various engineering scenarios. However, capturing accurate depth maps of transparent objects remains challenging due to their reflective and refractive properties, which pose difficulties for most commercial-grade optical sensors. In this paper, we propose a novel depth estimation method called DFNet-Trans, designed to estimate depth from a noisy RGB-D image input. Initially, a multiscale feature fusion module (FFM) is incorporated into the existing depth estimation network to generate the initial depth map. Subsequently, we enhance the network by adding a confidence branch and a mask branch on the same encoder, enabling improved distortion correction and real scene restoration in the depth estimation. Based on the framework representation, missing depth can be completed. Comprehensive experiments demonstrate that the proposed approach significantly outperforms the current state-of-the-art methods on the recently popular large-scale real dataset TransCG. the proposed approach achieves a remarkable 27.7% reduction in RMSE and a notable 34.6% reduction in REL. The generalization experiment shows that the proposed approach outperforms existing methods when generalized to an unknown real dataset.