To improve the remanufacturing efficiency of used mechanical parts and detect their surface failure state comprehensively, this paper proposes a color three-dimensional (3D) reconstruction technique using an improved semi-global matching (SGM). Traditional SGM algorithms often produce unsatisfactory parallax maps owing to the complex structure of mechanical parts and large illumination interference in a real environment. Addressing this, we propose a multifeature cost fusion strategy, which integrates color cost, Census transform, and gradient data to establish a more comprehensive similarity measurement function to improve the reliability of the initial cost. Furthermore, in the process of cost aggregation, a gradient threshold distinguishes between weak textures and edge regions. Moreover, we employ the Gaussian function to determine the distance weight of the local cross region to improve the algorithm's matching performance in the image light distortion region, weak part texture, and complex edge structures. Finally, we obtain a refined parallax map using parallax calculation and multistep parallax optimization. Besides, by merging this with an RGB image via texture mapping, we achieve a 3D color model reconstruction for used machinery components. Experimental outcomes indicate that the proposed method offers precise texture details and minimal size error for 3D color models and can be applied to the online detection of the surface failure information of used mechanical parts in the real production line.