Intel Corporation

Rene Ranftl

Vladlen Koltun

Matthias Mueller

Alexey Bochkovskiy

Diana Wofk

Methods, apparatus, systems, and articles of manufacture are disclosed for metric depth estimation using a monocular visual-inertial system. An example apparatus for metric depth estimation includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to access a globally-aligned depth prediction, the globally-aligned depth prediction generated based on a monocular depth estimator, access a dense scale map scaffolding, the dense scale map scaffolding generated based on visual-inertial odometry, regress a dense scale residual map determined using the globally-aligned depth prediction and the dense scale map scaffolding, and apply the dense scale residual map to the globally-aligned depth prediction.

Methods and apparatus for metric depth estimation using a monocular visual-inertial system

Methods, apparatus, systems and articles of manufacture disclosed herein perform dense prediction of an input image using transformers at an encoder stage and at a reassembly stage of an image processing system. A disclosed apparatus includes an encoder with an embedder to convert an input image to a plurality of tokens representing features extracted from the input image. The tokens are embedded with a learnable position embedding. The encoder also includes one or more transformers configured in a sequence of stages to relate the tokens to each other. The apparatus further includes a decoder that includes one or more of reassemblers to assemble the tokens into feature representations, one or more of fusion blocks to combine the feature representations to generate a final feature representation, and an output head to generate a dense prediction based on the final feature representation and based on an output task.

Methods and apparatus to perform dense prediction using transformer blocks

Systems, apparatuses and methods may provide for technology that generates, by a first neural network, an initial set of model weights based on input data and iteratively generates, by a second neural network, an updated set of model weights based on residual data associated with the initial set of model weights and the input data. Additionally, the technology may output a geometric model of the input data based on the updated set of model weights. In one example, the first neural network and the second neural network reduce the dependence of the geometric model on the number of data points in the input data.

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Munich, Germany

Rene Ranftl