Depth Estimation using Self-Supervised Learning from Monocular Images

Researched and trained a self-supervised learning model for depth estimation on KITTI dataset by experimenting with various iterations of UNet Architecture involving Encoders (ResNet, ConvNext ) and Up-sampling decoders like ESPCN achieving a rms of 3.7
Enhanced our model using Mask R-CNN pretrained on COCO Dataset to get semantic map which helped us reduce artifact problem in the disparity map and consequently reducing the overall loss for the model to 3.485