The surface flatness inspection of rail vehicles is key to improving the level of painting engineering and is directly related to aesthetics, anticorrosion, and traffic safety. An adaptive flatness inspection algorithm based on point cloud data is proposed to solve the problems of low accuracy and poor efficiency in detecting the flatness of sidewall panels of rail vehicles. The data to run the algorithm originates from an in-house-designed front-end data-scanning device, which is used to three-dimensionally scan the surfaces of the sidewall panels by sliding multiple laser scanners to obtain point cloud data. Subsequently, an improved guided-filtering algorithm is used to calculate the spatial geometric feature weights of the point cloud by integrating the distance, curvature, and normal vector to smoothen the single-frame point cloud, thus improving the point cloud quality. Next, on the basis of the contour-matching algorithm, the Hough straight-line detection algorithm is improved to segment the contour region of the point cloud, thus realizing the adaptive effect. Finally, the defective areas on the surfaces of the sidewall panels are extracted and localized, and quantification is performed. The results show that the algorithm can identify all the defective regions and ensure that the measurement depth error is within 0.3 mm, the measurement length and width error are within 1 mm, and operation speed satisfies the requirements of real-time detection of 30 mm/s.