Ghost-Stereo: GhostNet-based Cost Volume Enhancement and Aggregation for Stereo Matching Networks

TL;DR

Ghost-Stereo introduces a GhostNet-based stereo matching network that enhances cost volume processing with reduced parameters and faster computation, achieving competitive accuracy and better generalization on benchmarks.

Contribution

The paper proposes Ghost-Stereo, a novel end-to-end stereo matching network utilizing GhostNet for cost volume enhancement and aggregation, reducing complexity while maintaining high performance.

Findings

Achieves comparable accuracy to state-of-the-art real-time methods.

Reduces computational complexity with GhostNet-inspired modules.

Demonstrates better generalization on benchmark datasets.

Abstract

Depth estimation based on stereo matching is a classic but popular computer vision problem, which has a wide range of real-world applications. Current stereo matching methods generally adopt the deep Siamese neural network architecture, and have achieved impressing performance by constructing feature matching cost volumes and using 3D convolutions for cost aggregation. However, most existing methods suffer from large number of parameters and slow running time due to the sequential use of 3D convolutions. In this paper, we propose Ghost-Stereo, a novel end-to-end stereo matching network. The feature extraction part of the network uses the GhostNet to form a U-shaped structure. The core of Ghost-Stereo is a GhostNet feature-based cost volume enhancement (Ghost-CVE) module and a GhostNet-inspired lightweight cost volume aggregation (Ghost-CVA) module. For the Ghost-CVE part, cost volumes are constructed and fused by the GhostNet-based features to enhance the spatial context awareness. For the Ghost-CVA part, a lightweight 3D convolution bottleneck block based on the GhostNet is proposed to reduce the computational complexity in this module. By combining with the context and geometry fusion module, a classical hourglass-shaped cost volume aggregate structure is constructed. Ghost-Stereo achieves a comparable performance than state-of-the-art real-time methods on several publicly benchmarks, and shows a better generalization ability.