3D SEMANTIC PLANE DETECTION AND RECONSTRUCTION
This application is directed to reconstructing three-dimensional (3D) planes from image frames without supervision. A sequence of image frames are processed using a neural network model to provide one or more 3D plane parameters and one or more plane depth maps without 3D ground truth labels. A loss function combines the one or more 3D plane parameters and the one or more plane depth maps of the sequence of image frames. Based on the loss function, the neural network model is trained in an unsupervised manner for the one or more 3D plane parameters and the one or more plane depth maps. The trained neural network model is provided to a client device to process input images. Optionally, the loss function includes a photometric loss, a depth smoothness loss, and a depth consistency loss and is minimized during training to determine the 3D plane parameters and plane depth maps.