Accelerated Coordinate Encoding: Learning to Relocalize in Minutes Using RBG and Poses

A non-transitory computer-readable medium comprising stored instructions that, when executed by one or more computing devices, cause the one or more computing devices to collectively:

• receive a set of training images of one or more environments and corresponding metadata, the metadata comprising camera pose and intrinsics;
• train a relocalizer model using the set of training images, the relocalizer model configured to predict scene coordinates corresponding to pixels in an image of an environment;
• wherein the relocalizer model comprises a scene-agnostic convolutional network and a scene-specific regression network;
• receive a set of query images of an environment;
• apply a trained relocalizer model to the set of query images of the environment to generate predicted scene coordinates corresponding to the pixels in the query image;
• and
• apply a pose solver algorithm to the predicted scene coordinates to generate a camera pose.

A computer system, comprising:

• one or more computer processors;
• and
• one or more memories comprising stored instructions that when executed by the one or more computer processors causes the computer system to:
• receive a set of training images of one or more environments and corresponding metadata, the metadata comprising camera pose and intrinsics;
• train a relocalizer model using the set of training images, the relocalizer model configured to predict scene coordinates corresponding to pixels in an image of an environment;
• wherein the relocalizer model comprises a scene-agnostic convolutional network and a scene-specific regression network;
• receive a set of query images of an environment;
• apply a trained relocalizer model to the set of query images of the environment to generate predicted scene coordinates corresponding to the pixels in the query image;
• and
• apply a pose solver algorithm to the predicted scene coordinates to generate a camera pose.