"Guangzhou Street"

"The Guangzhou Street Dataset is a real-world binocular stereo vision dataset specifically curated to advance research in stereo matching and dense depth estimation. Captured within the complex and dynamic urban environments of Guangzhou, China, this locally sourced dataset provides high-quality stereo image pairs depicting a wide array of typical urban infrastructure. Key visual elements include vehicles, pedestrians, traffic poles, and street-level architectural structures, which inherently encompass challenging matching scenarios such as occlusions, textureless surfaces, and sharp object boundaries.The primary objective of this dataset is to serve as a robust evaluation benchmark for verifying the cross-domain adaptability and generalization capabilities of deep learning-based stereo networks in authentic, real-world deployments. By bridging the gap between standard synthetic or idealized training benchmarks and highly variable physical environments, the Guangzhou Street Dataset enables researchers to rigorously validate the reliability of intelligent vision systems. It is particularly valuable for evaluating depth estimation models tailored for autonomous driving, smart city infrastructure, and resource-constrained edge computing applications."