With the development of digital mapping cameras and image-matching techniques

highly dense point clouds can be automatically obtained by stereo image matching.These point clouds can be applied to 3D building reconstruction and DEM/DSM generation.However

owing to certain factors

such as textural differences

geometric distortions

and shadows

point clouds sustain gross errors.To address this problem

a semivariogram-based weight moving least square（SWMLS） method is proposed for gross error detection in point clouds obtained by stereo image matching.The SWMLS method is an iterative process.For each iteration

a semivariogram is used to estimate in advance the covariance between points in different distances and aspects.The weight can be calculated based on the estimated covariance and applied to approximate the least square and terrain surface separately.The distance between each point and the surface is calculated with the approximated local parametric surface

and the distance-constrained histogram method is used to detect the gross errors.Ingross error detection

the least square approximation move through the entire region of the point cloud by the same steps in the x-axis and y-axis.In this paper

we focus on discussing the adaptability of semivariogram to the digital elevation model and its variation across different terrains.The A3 system-generated point clouds of two datasets of urban and mountainous area ares used in the experiments.The point cloud for the urban area contains a 4.00%gross error

whereas the point cloud for the mountainous area contains 0.19%gross error.To test the efficiency and advantages of our method

the results of our method and those of the equal weight based MLS（EWMLS） method are compared manually to the detected results.Experimental results show that both SWMLS and EWMLS have removed all the gross errors in the point cloud.However

the results of a further quantitative analysis suggest that SWMLS is more accurate than EWMLS.For the urban area

the misclassification by S WMLS is 2.07%

whereas that by EWMLS is 7.72%.For the mountainous area

EWMLS has committed 2.56%misclassification

whereas our proposed method generated a reduced misclassification（0.76%）.We may draw the conclusion as below.（1） As a regionalized method

semivariogram is efficient in weight estimation in MLS.（2）Both SWMLS and EWMLS are effective in detecting gross errors in point clouds generated from stereo images.However

both methods may commit few misclassifications for urban and mountainous areas.（3）Compared with EWMLS

SWMLS generated less misclassification in both datasets

suggesting that SWMLS demonstrates a better performance in the gross error detection in stereo-matched point clouds.