A computer-implemented method for automated orientation estimation of a physical asset, comprising receiving, by a processor, plurality of images of a physical asset captured from multiple view angles using an image capturing device and sensor data of the image capturing device, wherein the sensor data is associated with the plurality of images and comprises position data and orientation data of the image capturing device; generating, by the processor, one or more segmentation masks of the physical asset in each of the plurality of images; tracking, by the processor, the generated one or more segmentation masks of the physical asset across the plurality of images; composing, by the processor, a 3-dimensional model of the physical asset based on the one or more segmentation masks tracked across the plurality of images; and determining, by the processor, the orientation of the physical asset by computing cardinal axis of the 3-dimensional model, wherein the generation of one or more segmentation masks of the physical asset, comprises: extracting, by the processor, one or more image patches by identifying at least one region of interest from each of the plurality of images, using a first neural network model; determining, by the processor, pixel level boundaries of the physical asset in the extracted one or more image patches using a second neural network model; and generating, by the processor, one or more segmentation masks for the determined pixel level boundaries using the second neural network model. receiving, by a processor, plurality of images of a physical asset captured from multiple view angles using an image capturing device and sensor data of the image capturing device, wherein the sensor data is associated with the plurality of images and comprises position data and orientation data of the image capturing device; generating, by the processor, one or more segmentation masks of the physical asset in each of the plurality of images; tracking, by the processor, the generated one or more segmentation masks of the physical asset across the plurality of images; composing, by the processor, a 3-dimensional model of the physical asset based on the one or more segmentation masks tracked across the plurality of images; and determining, by the processor, the orientation of the physical asset by computing cardinal axis of the 3-dimensional model, wherein the generation of one or more segmentation masks of the physical asset, comprises: extracting, by the processor, one or more image patches by identifying at least one region of interest from each of the plurality of images, using a first neural network model; determining, by the processor, pixel level boundaries of the physical asset in the extracted one or more image patches using a second neural network model; and generating, by the processor, one or more segmentation masks for the determined pixel level boundaries using the second neural network model. extracting, by the processor, one or more image patches by identifying at least one region of interest from each of the plurality of images, using a first neural network model; determining, by the processor, pixel level boundaries of the physical asset in the extracted one or more image patches using a second neural network model; and generating, by the processor, one or more segmentation masks for the determined pixel level boundaries using the second neural network model. <EOS>