Abstract: Depth maps are generated based on a sequence of posed images captured by a camera, the depth maps are fused into a truncated signed distance function (TSDF), and an initial estimate of 3-dimensional (3D) scene geometry is generated by extracting a 3D mesh via the TSDF. 3D embeddings are estimated for each vertex in the 3D mesh by mapping each vertex to a multi-view consistent plane embedding space such that vertices on a same plane map to nearly a same place in the embedding space. The vertices are clustered into 3D plane instances based on respective 3D embeddings and geometry information defined by the 3D mesh to create a planar representation of the scene. A location of a virtual element in a virtual world of an augmented reality game is determined based on the planar representation.

Abstract: A computer-implemented method is disclosed for generating scene reconstructions from image data. The method includes: receiving image data of a scene captured by a camera; inputting the image data of the scene into a scene reconstruction model; receiving, from the scene reconstruction model, a final spatial model of the scene, wherein the scene reconstruction model generates the final spatial model by: predicting a depth map for each image of the image data, extracting a feature map for each image of the image data, generating a first spatial model based on the predicted depth maps of the images, generating a second spatial model based on the extracted feature maps of the images, and determining the final spatial model by combining the first spatial model and the second spatial model; and providing functionality on a computing device related to the scene and based on the final spatial model.

Abstract: A method for training a depth estimation model and methods for use thereof are described. Images are acquired and input into a depth model to extract a depth map for each of the plurality of images based on parameters of the depth model. The method includes inputting the images into a pose decoder to extract a pose for each image. The method includes generating a plurality of synthetic frames based on the depth map and the pose for each image. The method includes calculating a loss value with an input scale occlusion and motion aware loss function based on a comparison of the synthetic frames and the images. The method includes adjusting the plurality of parameters of the depth model based on the loss value. The trained model can receive an image of a scene and generate a depth map of the scene according to the image.

Abstract: A system generates augmented reality content by generating an occlusion mask via implicit depth estimation. The system receives input image(s) of a real-world environment captured by a camera assembly. The system generates a feature map from the input image(s), wherein the feature map comprises abstract features representing depth of object(s) in the real-world environment. The system generates an occlusion mask from the feature map and a depth map for the virtual object. The depth map for the virtual object indicates a depth of each pixel of the virtual object. The occlusion mask indicates pixel(s) of the virtual object that are occluded by an object in the real-world environment. The system generates the composite image based on a first input image at a current timestamp, the virtual object, and the occlusion mask. The composite image may then displayed on an electronic display.

Abstract: A scene reconstruction model is disclosed that outputs a heightfield for a series of input images. The model, for each input image, predicts a depth map and extracts a feature map. The model builds a 3D model utilizing the predicted depth maps and camera poses for the images. The model raycasts the 3D model to determine a raw heightfield for the scene. The model utilizes the raw heightfield to sample features from the feature maps corresponding to positions on the heightfield. The model aggregates the sampled features into an aggregate feature map. The model regresses a refined heightfield based on the aggregate feature map. The model determines the final heightfield based on a combination of the raw heightfield and the refined heightfield. With the final heightfield, a client device may generate virtual content augmented on real-world images captured by the client device.

Abstract: A method for training a first neural network to detect the viewpoint of an object visible on an image and belonging to a given category of object when this image is inputted to the first neural network, including: providing a dataset of pairs of images under different viewpoints, providing a second neural network configured to be able to deliver appearance information of an object, providing a third neural network configured to be able to deliver a synthetic image of an object of the category using appearance information and a viewpoint, jointly training the first neural network, the second neural network, and the third neural network.

Abstract: A multi-frame depth estimation model is disclosed. The model is trained and configured to receive an input image and an additional image. The model outputs a depth map for the input image based on the input image and the additional image. The model may extract a feature map for the input image and an additional feature map for the additional image. For each of a plurality of depth planes, the model warps the feature map to the depth plane based on relative pose between the input image and the additional image, the depth plane, and camera intrinsics. The model builds a cost volume from the warped feature maps for the plurality of depth planes. A decoder of the model inputs the cost volume and the input image to output the depth map.

Abstract: A computer system generates stereo image data from monocular images. The system generates depth maps for single images using a monocular depth estimation method. The system converts the depth maps to disparity maps and uses the disparity maps to generate additional images forming stereo pairs with the monocular images. The stereo pairs can be used to form a stereo image training data set for training various models, including depth estimation models or stereo matching models.

Abstract: A method for training a depth estimation model and methods for use thereof are described. Images are acquired and input into a depth model to extract a depth map for each of the plurality of images based on parameters of the depth model. The method includes inputting the images into a pose decoder to extract a pose for each image. The method includes generating a plurality of synthetic frames based on the depth map and the pose for each image. The method includes calculating a loss value with an input scale occlusion and motion aware loss function based on a comparison of the synthetic frames and the images. The method includes adjusting the plurality of parameters of the depth model based on the loss value. The trained model can receive an image of a scene and generate a depth map of the scene according to the image.

Abstract: A method for training a depth estimation model and methods for use thereof are described. Images are acquired and input into a depth model to extract a depth map for each of the plurality of images based on parameters of the depth model. The method includes inputting the images into a pose decoder to extract a pose for each image. The method includes generating a plurality of synthetic frames based on the depth map and the pose for each image. The method includes calculating a loss value with an input scale occlusion and motion aware loss function based on a comparison of the synthetic frames and the images. The method includes adjusting the plurality of parameters of the depth model based on the loss value. The trained model can receive an image of a scene and generate a depth map of the scene according to the image.

Abstract: A method for training a depth estimation model and methods for use thereof are described. Images are acquired and input into a depth model to extract a depth map for each of the plurality of images based on parameters of the depth model. The method includes inputting the images into a pose decoder to extract a pose for each image. The method includes generating a plurality of synthetic frames based on the depth map and the pose for each image. The method includes calculating a loss value with an input scale occlusion and motion aware loss function based on a comparison of the synthetic frames and the images. The method includes adjusting the plurality of parameters of the depth model based on the loss value. The trained model can receive an image of a scene and generate a depth map of the scene according to the image.

Abstract: Systems and methods are described for predicting depth from colour image data using a statistical model such as a convolutional neural network (CNN), The model is trained on binocular stereo pairs of images, enabling depth data to be predicted from a single source colour image. The model is trained to predict, for each image of an input binocular stereo pair, corresponding disparity values that enable reconstruction of another image when applied, to the image. The model is updated based on a cost function that enforces consistency between the predicted disparity values for each image in the stereo pair.