3D OBJECTS OCCLUSION ESTIMATION USING MONTE CARLO SIMULATION

Monte Carlo simulation is applied in many fields of science and remains a useful tool for complex testing experiments under uncertain conditions. 3D object occlusion estimation remains the most challenging task in the field of computer vision, with multiple impacts on autonomous driving, robotics, pedestrian detection and virtual reality. In this paper, we presented experiments with five groups of arbitrary 3D objects in Euclidean Space and estimated the area of the occluded region between 3D objects using the Monte Carlo method. The objects vertices data points were obtained from [CUST] and the simulation was carried out with MATLAB R2019b.