This paper proposes a point cloud simplification method based on local averaging to address the problem of a large number of noise points in the actual point cloud data obtained from rail wear measurement using structured light technology, which are caused by factors such as railway operation environment interference, high gloss areas on the rail surface, and equipment problems, and seriously affect the accuracy and efficiency of subsequent rail wear calculations, therefore, a point cloud simplification method based on local averaging is proposed. The method generates a simplified point cloud by traversing each point in the point cloud and calculating the average position of all points within the circle using an enclosing circle of specified radius. The experimental results show that the proposed method is significantly superior to traditional statistical filtering and radius filtering algorithms in terms of noise reduction and preservation of rail profile details. The average noise reduction rate reaches 0.832 0, which is about 4.3 times higher than statistical filtering and 15 times higher than radius filtering. Meanwhile, the average error of wear calculation is only 0.025 01 mm, which is about 95.7% lower than statistical filtering and 85.1% lower than radius filtering. In terms of processing efficiency, the average time consumption of this method is only 0.006 5 ms, which is significantly better than other methods. This method can effectively reduce the amount of point cloud data, preserve the rail profile features to the maximum extent, and meet the measurement needs of rail wear.