Abstract: Methods, systems, apparatus, and articles of manufacture for monocular depth estimation are disclosed. An example apparatus disclosed herein is to determine bin center positions for a pixel of an image based on first features obtained at a first decoder layer of an encoder-decoder architecture, the image representative of a three-dimensional (3D) scene, the bin center positions corresponding to respective different metric depth values. The example apparatus is also to adjust the bin center positions at a second decoder layer of the encoder-decoder architecture based on an attractor point, the attractor point based on the first features and second features obtained at the second decoder layer. The example apparatus is further to output a metric depth map corresponding to the image, the metric depth map including a metric depth value corresponding to the pixel, the metric depth value based on the adjusted bin center positions.

Abstract: Machine learning models can generate outputs such as salient object detection. However, top-performing large-scale models have high computational cost, making the models hard to deploy on resource-constrained devices and to make predictions in real time. A difference convolution reparameterization technique can be used to fuse the sets of parameters learned during training time into a single set of parameters to be used at inference time or at deployment. The technique can increase performance without adding a lot of parameters. Furthermore, an efficient and effective spatial temporal difference convolution block can be used to leverage temporal information to make predictions. The spatial temporal difference convolution block can also apply the difference convolution reparameterization technique to reduce the need to increase parameters to achieve desired performance.

Abstract: 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.