This research develops an attribute-enriched dense image matching framework to generate high-precision 3D point cloud information characterized by enhanced geometric fidelity and semantic interpretability. Traditional dense reconstruction often suffers from radiometric ambiguities and structural distortions in complex urban scenes, such as those with repetitive industrial patterns and specular reflections. To overcome these limitations, the proposed pipeline integrates high-level semantic attributes with multi-scale geometric features, including keypoints, edges, and structural vertices.&nbsp;<br />These attributes serve as reliable conjugate feature seeds to initialize a sparse disparity map, which is subsequently densified via Weighted Inverse Distance Weighting and refined through adaptive Normalized Cross-Correlation. During the dense matching phase, a dual-layer constraint mechanism, comprising attribute-dependent searching ranges and class-consistency validation, is enforced to mitigate cross-object mismatches.&nbsp;<br />Experimental results demonstrate that the framework successfully transforms geometric coordinates into verifiable information assets. By inheriting 2D image metadata, the resulting point clouds provide an intuitive interface for operators to rapidly identify critical features. Furthermore, the system utilizes an Attribute Table to facilitate a reprojection verification process, effectively transforming redundant observations from multiple stereo pairs into a robust metric for cross-validation. This integration ensures a geometrically coherent and queryable foundation for automated feature extraction and high-precision mapping tasks.