SELF-SUPERVISED INCREMENTAL TRAINING OF A RELOCALIZER MODEL
A relocalizer model for an environment is trained using an iterative process. To initialize the relocalizer model, an initial image is registered with its camera pose established as the reference. In each subsequent iteration of training, the relocalizer model is applied to additional images to predict pose estimates for the images. The images and their pose estimates are then leveraged in retraining of the relocalizer model. In general, the training of the relocalizer model entails extracting scene coordinates for pixels of a training image. The scene coordinates are then projected into a projection based on the pose estimate of the training image. A loss is calculated between the projection and the training image. And parameters of the relocalizer model are adjusted to minimize the loss. The iterative training may continue until an end condition is met. The trained relocalizer model is configured to input an image of the environment and to output the camera pose for the image.
1 . A method for relocalization of a target image with a relocalizer model comprising:
receiving a target image depicting a portion of a real-world environment;
determining a camera pose of the target image by applying the relocalizer model to the target image, wherein the relocalizer model was trained by:
obtaining image data comprising a plurality of training images captured by one or more cameras depicting the real-world environment;
identifying an initial training image from the image data for initializing the relocalizer model;
training the relocalizer model with the initial training image and an identity pose establishing a pose of the initial training image as reference;
performing an incremental training process comprising, in each of one or more iterations:
applying the relocalizer model to a subsequent set of training images from the image data to output a pose estimate for each training image in the subsequent set, and
retraining the relocalizer model using the subsequent set of training images and the pose estimates;
measuring performance of the relocalizer model with a validation set of training images from the image data; and
responsive to determining that an end condition is met based on the performance of the relocalizer model, ending the incremental training process; and
providing artificial reality content using the target image and based on the camera pose output by the relocalizer model.
2 . The method of claim 1 , wherein identifying the initial training image from the image data comprises randomly selecting the initial training image from the image data.
3 . The method of claim 1 , wherein training the relocalizer model with the initial training image comprises:
applying a reconstruction network to features of the initial training image to output a plurality of sets of scene coordinates representing spatial locations of pixels associated with one or more real-world objects in the real-world environment;
projecting the plurality of sets of scene coordinates into a two-dimensional projection based on the identity pose of the initial training image;
determining a loss between the two-dimensional projection and the initial training image; and
adjusting parameters of the reconstruction network to minimize the loss.
4 . The method of claim 3 , wherein training the relocalizer model comprises:
applying a feature network to the initial training image data to output the features of the initial training image.
5 . The method of claim 4 , wherein the feature network, the reconstruction network, or both are neural network models.
6 . The method of claim 3 , wherein determining the loss comprises computing a pixel-wise projection error between corresponding pixels of the two-dimensional projection and pixels of the initial training image.
7 . The method of claim 1 , wherein applying the relocalizer model to the subsequent set of training images data to output the pose estimate for each training image during the incremental training process comprises, for each training image:
applying a reconstruction network to features of the training image to output a plurality of sets of scene coordinates representing spatial locations of pixels associated with one or more real-world objects in the real-world environment;
determining a pose estimate for the training image based on the plurality of sets of scene coordinates output by the reconstruction network.
8 . The method of claim 7 , wherein determining the pose estimate for the training image comprises applying a perspective-n-point algorithm to the plurality of sets of scene coordinates to determine the pose estimate.
9 . The method of claim 7 , wherein retraining the relocalizer model using the subsequent set of training images and the pose estimates during the incremental training process comprises, for each training image:
applying a pose refinement network to modify the pose estimate of the training image to yield a refined pose estimate;
projecting the plurality of sets of scene coordinates into a two-dimensional projection for the training image based on the refined pose estimate of the training image;
determining a loss between the two-dimensional projection and the corresponding training image; and
adjusting parameters of the reconstruction network and the pose refinement network to minimize the loss.
10 . The method of claim 7 , wherein retraining the relocalizer model using the subsequent set of training images and the pose estimates during the incremental training process further comprises, for each training image:
applying dropout of one or more features, wherein the reconstruction network is applied to remaining features of the training image.
11 . The method of claim 7 , applying the relocalizer model to the subsequent set of training images data to output the pose estimate for each training image during the incremental training process further comprises, for each training image:
determining a confidence associated with the determined pose estimate based on the plurality of sets of scene coordinates;
responsive to determining the confidence is below a confidence threshold, removing the training image from the subsequent set prior to retraining the relocalizer model.
12 . The method of claim 1 , wherein providing the artificial reality content using the target image and based on the camera pose comprises:
generating augmented reality content by augmenting the target image with one or more virtual elements based on the camera pose output by the relocalizer model; and
providing the augmented reality content.
13 . The method of claim 1 , wherein providing the artificial reality content using the target image and based on the camera pose comprises:
generating a scene reconstruction based in part on the target image and the camera pose output by the relocalizer model; and
providing the scene reconstruction.
14 . A method for training a relocalizer model, comprising:
obtaining image data comprising a plurality of training images captured by a camera of a real-world environment;
identifying an initial training image from the image data for initializing the relocalizer model;
training the relocalizer model with the initial training image and an identity pose establishing a pose of the initial training image as reference;
performing an incremental training process comprising:
applying the relocalizer model to a subsequent set of training images from the image data to output a pose estimate for each training image in the subsequent set, and
retraining the relocalizer model using the subsequent set of training images and the pose estimates;
measuring performance of the relocalizer model with a validation set of training images from the image data; and
responsive to determining that an end condition is met based on the performance of the relocalizer model, ending the incremental training process.
15 . The method of claim 14 , wherein training the relocalizer model with the initial training image comprises:
applying a reconstruction network to features of the initial training image to output a plurality of sets of scene coordinates representing spatial locations of pixels associated with one or more real-world objects in the real-world environment;
projecting the plurality of sets of scene coordinates into a two-dimensional projection based on the identity pose of the initial training image;
determining a loss between the two-dimensional projection and the initial training image; and
adjusting parameters of the reconstruction network to minimize the loss.
16 . The method of claim 15 , wherein training the relocalizer model comprises:
applying a feature network to the initial training image data to output the features of the initial training image.
17 . The method of claim 14 , wherein applying the relocalizer model to the subsequent set of training images data to output the pose estimate for each training image during the incremental training process comprises, for each training image:
applying a reconstruction network to features of the training image to output a plurality of sets of scene coordinates representing spatial locations of pixels associated with one or more real-world objects in the real-world environment;
determining a pose estimate for the training image based on the plurality of sets of scene coordinates output by the reconstruction network.
18 . The method of claim 17 , wherein determining the pose estimate for the training image comprises applying a perspective-n-point algorithm to the plurality of sets of scene coordinates to determine the pose estimate.
19 . The method of claim 17 , wherein retraining the relocalizer model using the subsequent set of training images and the pose estimates during the incremental training process comprises, for each training image:
applying a pose refinement network to modify the pose estimate of the training image to yield a refined pose estimate;
projecting the plurality of sets of scene coordinates into a two-dimensional projection for the training image based on the refined pose estimate of the training image;
determining a loss between the two-dimensional projection and the corresponding training image; and
adjusting parameters of the reconstruction network and the pose refinement network to minimize the loss.
20 . The method of claim 17 , applying the relocalizer model to the subsequent set of training images data to output the pose estimate for each training image during the incremental training process further comprises, for each training image:
determining a confidence associated with the determined pose estimate based on the plurality of sets of scene coordinates;
responsive to determining the confidence is below a confidence threshold, removing the training image from the subsequent set prior to retraining the relocalizer model.