Lightweight Stereo Matching for Real-Time Applications With 2D Cost Volume Aggregation

Despite the significant advancements in learning-based stereo matching algorithms, a significant challenge remains: the high computational cost and memory demands of 3D convolutions, which hinder real-time deployment on resource-constrained platforms like edge devices. In this paper, we present a novel approach that completely avoids the use of 3D convolutions, with the goal of achieving faster inference speeds while maintaining comparable accuracy to existing state-of-the-art methods. Our proposed solution revolves around a 2D cost aggregation technique, which serves as a viable alternative to traditional 3D convolutions, delivering similar results in terms of accuracy. This innovative method significantly reduces computational overhead, facilitating more efficient resource utilization and paving the way for real-time applications. Complementing our 2D cost aggregation module, we introduce a multi-stage feature extractor, designed to enhance feature representation while remaining straightforward and lightweight. The integration of the 2D cost aggregation and multi-stage feature extraction results in an efficient architecture for cost aggregation, simplifying the model and ensuring computational efficiency without sacrificing accuracy. This framework delivers high-performance stereo matching suitable for devices with limited computational capabilities. Through evaluation on benchmark datasets, we demonstrate the effectiveness of our approach, highlighting its potential for real-time 3D perception in applications. By addressing the constraints imposed by 3D convolutions and offering a more pragmatic alternative, this work bridges the gap between high-performance stereo matching algorithms and the realities of edge computing environments.