Shree, R.Madagaonkar, S.B.Singh, M.Chandra, M.T.A.Rathnamma, M.V.Venkataramana, V.Chandrasekaran, K.2026-02-0620222022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022, 2022, Vol., , p. -https://doi.org/10.1109/ICCCNT54827.2022.9984354https://idr.nitk.ac.in/handle/123456789/29818Depth estimation is fundamental in upcoming technology advancements like scene understanding, robot vision, intelligent driver assistance systems, and many new technologies. Estimating the depth of objects from a viewport can be achieved using various mathematical, geometrical, and stereo concepts, but the process is unaffordable and erroneous. Depth estimation from a single can be accurately done using neural networks. Although this is a challenging task, researchers around the globe have published various works. The works include different neural network standards like CNN, GANs, Encoder-Decoder. The paper analyses and examines famous works in this field of study. Later in the paper, a comparative survey of depth estimation approaches using neural networks is done. © 2022 IEEE.CNNencoder-decoderGANimage processingmonocular depth estimationneural networksRGB-D datasetS2DNet