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Body measurement is an important link in monitoring the growth of livestock in breeding management. Conventional measurements are notoriously difficult, unstable, stressful and time-consuming. 3D Computer vision technology has demonstrated its superiority in body size measurement and live body weight estimation because it has advantages of being both non-contact and effective. The objective of this paper was validating the use of single-frame surface point clouds to measure the body size, and develop a model to estimate the pig weight. With a limit bar not applied, a Kinect camera was fixed above the drinking area allowed multiple images of pigs to be captured at one time in a group-house. Body size parameters were extracted from back surface point clouds, and subsequently used as independent variables to build the three regression analysis models: i) stepwise regression analysis, ii) ridge regression analysis, and iii) partial least squares regression analysis. An experiment was conducted on fifty pigs in a group-house, half of which were Landrace gilts, half were Yorkshire shoats. The result of size measurement showed the mean relative absolute errors of body length, height, shoulder width were 0.7%, 1.8%, and 3.3%, respectively. Final ridge regression equation was developed with an R 2 of 0.958 and a MAE of 2.961 kg. It was concluded that the proposed methods based on Kinect v2 was shown to be accurate and reliable for body size measurement and live body mass estimation. • Novel body size measurement method based on single view Kinect without limit bar presented. • Mean relative absolute errors of body length, height, shoulder width were acceptable. • Non-contact and low-cost measurement for small-scale farms was provided. • Scheme for data collection in intelligent breeding environment provided. • Ridge regression performed well in weight estimation R 2 of 0.958 & MAE of 2.961 kg. [ABSTRACT FROM AUTHOR]