  Can a neural network estimate an object's dimension in the wild? In this
paper, we propose a method and deep learning architecture to estimate the
dimensions of a quadrilateral object of interest in videos using a monocular
camera. The proposed technique does not use camera calibration or handcrafted
geometric features; however, features are learned with the help of coefficients
of a segmentation neural network during the training process. A real-time
instance segmentation-based Deep Neural Network with a ResNet50 backbone is
employed, giving the object's prototype mask and thus provides a region of
interest to regress its dimensions. The instance segmentation network is
trained to look at only the nearest object of interest. The regression is
performed using an MLP head which looks only at the mask coefficients of the
bounding box detector head and the prototype segmentation mask. We trained the
system with three different random cameras achieving 22% MAPE for the test
dataset for the dimension estimation
