DEPTH MAPS PREDICTION SYSTEM AND TRAINING METHOD FOR SUCH A SYSTEM

A depth maps prediction system comprising a neural network (1000) configured to receive images (I) of a scene at successive time steps (t−1, t, t+1, . . . ) and comprising three sub-networks: an encoder (100), a ConvLSTM (200) and a decoder (300).The neural network (1000) is configured so that at each time step:a) the encoder sub-network (100) processes an image (I) and outputs a low resolution initial image representation (X);b) the CONVLSTM sub-network (200) processes the initial image representation (X), values for a previous time step (t−1) of an internal state (C(t−1)) and of an LSTM hidden variable data (H(t−1)) of the ConvLSTM sub-network, and outputs updated values of the internal state (C(t)) and of the LSTM hidden variable data (H(t)); andc) the decoder sub-network (300) inputs the LSTM output data (LOD) and generates a predicted dense depth map (D″) for the inputted image (I).