Abstract: Abstract: The body size of the livestock is an important indicator for breeding and animal production, however, the traditional measurement of body size of the livestock is a hard manual operation and it is inaccurate, expensive and time-consuming. In the field of computer vision measurement, the location of the pig body size measurement points is an essential work. To solve the automatic extraction of pig body size measurement points based on point clouds, a method of automatic extraction of pig body size measurement points was developed based on rotation normalization approach. In order to acquire the measurement points of the livestock, several processing steps were applied, and the steps were as follows: (1) Random sample consensus algorithm was used to acquire the plane parameter of the ground and the ground calculated by the parameter was then removed. The target pig and the ground normal vector were acquired. (2) Since the livestock acquired was from different coordinate systems, we proposed a rotation normalization method. First, the method of principal component analysis was used to get the coordinate axis of data cloud of the pig body and the initial measuring coordinate system was obtained. Secondly, the direction of the y axis was replaced by the direction of the ground normal vector acquired. The normal direction had 2 possibilities, that was, the normal vector was either pointing straight to the pig or going back away from the pig. The direction of the normal vector was determined by whether there was intersection between the constructed cuboid region and the pig. The cuboid region was constructed from the point on the ground along the normal vector. Finally, by using the geometrical relationship among the coordinate axis, standard measuring coordinate system defined in this paper was acquired. (3) Some of the measurement points of the livestock were usually the extreme points on the livestock body contour. By cutting the cloud points of the pig along the x axis direction with a step length, the slice data of the pig were acquired and the point cloud of contour line was obtained. By using the structural relation between the measurements points and the geometric feature of the measurement points either at the contour points or on the whole pig, the measurements points were calculated. Landrace sow specimens with 100 days old were selected as the experimental samples. To validate the rotation normalization performance of the proposed method, the scene point cloud including the specimen was used to evaluate the effects objectively. The point cloud was acquired by the 3D (three-dimensional) camera Xtion sensor using KinectFusion technology. Xtion is a sensor based on technology of structured light and consists of an infrared emitter, an infrared camera and an RGB (red, green, blue) camera. KinectFusion provides a function of 3D object scanning using a sensor. The results indicated that our approach could produce a reasonable rotation normalization result, and the directions of the length, height and width of the pig were basically consistent to the directions of the x, y and z axis. In order to verify the precision of measurement point's position, the proposed method was applied to locate the neck midpoint, body length measuring point, body width measuring point, body height measuring point and hip height measuring point. Average error of the results between the automatic extraction and artificial measurement of the selected points was less than 16 mm. The method in this paper can provide a reference for the automatic body measurement of the point cloud of the pig. This work is expected to be a useful contribution for animal production and breeding genetics.