Robust Synthetic-to-Real Transfer for Stereo Matching

Abstract

With advancements in domain generalized stereo matching networks models pre-trained on synthetic data demonstrate strong robustness to unseen domains. However few studies have investigated the robustness after fine-tuning them in real-world scenarios during which the domain generalization ability can be seriously degraded. In this paper we explore fine-tuning stereo matching networks without compromising their robustness to unseen domains. Our motivation stems from comparing Ground Truth (GT) versus Pseudo Label (PL) for fine-tuning: GT degrades but PL preserves the domain generalization ability. Empirically we find the difference between GT and PL implies valuable information that can regularize networks during fine-tuning. We also propose a framework to utilize this difference for fine-tuning consisting of a frozen Teacher an exponential moving average (EMA) Teacher and a Student network. The core idea is to utilize the EMA Teacher to measure what the Student has learned and dynamically improve GT and PL for fine-tuning. We integrate our framework with state-of-the-art networks and evaluate its effectiveness on several real-world datasets. Extensive experiments show that our method effectively preserves the domain generalization ability during fine-tuning.