Depth maps prediction system and training method for such a system
US12026899B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Jul 22, 2019 |
| Grant date | Jul 2, 2024 |
| Priority date | — |
| Expiry date | Jul 13, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30252
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
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)); and c) the decoder sub-network (300) inputs the LSTM output data (LOD) and generates a predicted dense depth map (D″) for the inputted image (I).
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.