# A Non-Contact Measurement of Animal Body Size Based on Structured Light
## 1 Introduction  
The body size parameters of an animal can reflect its growth and health condition. It is also an essential indicator of selecting and breeding needs, for rare animal protection, and for judging the health condition of animals. The traditional way to obtain those parameters is to use tools such as measuring sticks and tapes to make contact with the animals directly (the so-called manual measurement). This method could be more efficient and easier. Human subjective factors easily influence the results, and the measurement may lead to animal stress reactions, which are dangerous to the operators. With the development of computer vision, how to use the linkage between computers and cameras to obtain three-dimensional point clouds on animal body measurement has aroused researchers' interest. Focused on a single animal, [PERSON] et al. [1] used four Kinect sensors to obtain the point clouds of a cow's body in four directions. [PERSON] et al. [2] built a depth camera-based system to automatically measure the cattle body parameters, and their average difference was 14.6%; they also used a depth camera and a 3D shape recovery method to obtain 103 point clouds of one cattle body [3] with a 90% confidence level. [PERSON] et al. [4] put a pig into a cave and used multi-view RGB-D cameras to obtain the point cloud. The average relative error was 4.67%. Focused on multiple animals, [PERSON] et al. [5] used depth cameras and key-point detection methods to experiment on cattle and pigs. They retrieved data on 103 cattle and nine pig and compared them with the manual results. It showed that the margin of error on cattle was above 20%, and the margin on pigs was above 30%. Other than using a depth camera, [PERSON] et al. [6] used an SR4K time-of-flight camera to determine the body traits of cows and had a very precise result, butthis method mostly requires the animal to stay still, so it has limited usage scenarios. [PERSON] et al. [7] proposed a new method to capture a detailed 3D shape of lions, tigers, and bears from images alone; they used a robust prior model of articulated animal shapes and deformed the animal shapes into a canonical reference pose. [PERSON] et al. [8] presented a new method to capture new bird species using an articulated template and images. They captured 17 bird species, and the mean PCK (percentage of correct key points) of these species was 0.963. [PERSON] [9] proposed the breed-augmented regression using the classification method to recover dogs' 3D shape and pose from a single image with a 0.913 PCK. [PERSON] [10] presented a new method dubbed MagicPony to predict the articulated animal's 3D shape, articulation, viewpoint, texture, and lighting, which had a PCK (percentage of correct key points) of 0.635. The method using structured light to obtain the surface point cloud of an object has been used mainly in 3D scanning. Focused on industrial products, [PERSON] et al. [11] designed a low-cost 3D scanner and used it for an archeological statue. [PERSON] et al. [12] used structured light to measure various object materials such as marble, wood, stone, etc. [PERSON] et al. [13] combined structured light with close-range photogrammetry to measure large-scale 3D shapes. [PERSON] et al. [14] used a high-precision air-floating rotary table and a structured light sensor to obtain the point cloud of gears. [PERSON] et al. [15] scanned modern skeletal material, which was non-movable, in a museum using a structured light scanner. [PERSON] et al. [16] used a Morpho3D camera based on structured light to retrieve the cow body point cloud; they set up the equipment on a sliding door, guided the cow to walk through the door slowly, and had an accuracy near 80%. However, this method needs to train and guide the animal and can only collect the data after a period of time with limited usage scenarios. The key point in using structured light to acquire the point cloud of an object is the calibration of the light plane. [PERSON] et al.