As a core technology in real-time video processing and intelligent surveillance, stereo matching provides essential depth perception capabilities for multimedia applications. However, high-precision stereo networks often come with significant computational costs, making real-time inference on power- and memory-constrained edge devices challenging. On the other hand, lightweight real-time networks still struggle with accuracy limitations. To address this challenge, we propose RCAENet, a high-performance stereo network designed for real-time and high-accuracy depth estimation on edge devices. To enhance feature extraction efficiency, we introduce the Residual Convolutional Feature Extraction (RCFE) module, which replaces conventional convolutional layers to capture more expressive features while maintaining computational efficiency. Additionally, we propose the Enhanced Adaptive Upsampling (EAU) module, which integrates channel and spatial attention mechanisms to improve feature fusion and disparity refinement. Furthermore, we design an Enhanced 3D CNN (E3DC) along with the Cost Aggregation and Residual Attention (CA-ResAgg) module for cost volume regularization. This module incorporates residual aggregation and efficient channel attention to further enhance disparity estimation accuracy. Built upon these components, RCAENet features a multi-scale architecture that effectively balances accuracy and efficiency. Extensive experiments demonstrate that these innovations enable RCAENet to achieve real‑time inference on edge devices while maintaining state‑of‑the‑art depth accuracy.
Liang et al. (2026) studied this question.