Niantic, Inc.

Niantic Spatial, Inc.

Michael David Firman

Gabriel J Brostow

James Watson

Daniyar Turmukhambetov

Clément Godard

Oisin MacAodha

Victor Adrian Prisacariu

Aron Monszpart

Eric Brachmann

Karren Dai Yang

Sara Alexandra Gomes Vicente

Michaël Lalaina Ramamonjisoa

Oisin Mac Aodha

A reference image and recorded sound of an environment of a client device are obtained. The recorded sound may be captured by a microphone of the client device in a period of time after generation of a localization sound by the client device. The location of the client device in the environment may be determined using the reference image and the recorded sound.

Localization using audio and visual data

A depth prediction model for predicting a depth map from an input image is disclosed. The depth prediction model leverages wavelet decomposition to minimize computations. The depth prediction model comprises a plurality of encoding layers, a coarse prediction layer, a plurality of decoding layers, and a plurality of inverse discrete wavelet transforms (IDWTs). The encoding layers are configured to input the image and to downsample the image into feature maps including a coarse feature map. The coarse depth prediction layer is configured to input the coarse feature map and to output a coarse depth map. The decoding layers are configured to input the feature maps and to predict wavelet coefficients based on the feature maps. The IDWTs are configured to upsample the coarse depth map based on the predicted wavelet coefficients to the final depth map at the same resolution as the input image.

Image depth prediction with wavelet decomposition

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.

High-speed real-time scene reconstruction from input image data

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.

Self-supervised multi-frame monocular depth estimation model

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.

Self-supervised training of a depth estimation system

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.

Generating stereo image data from monocular images

A model predicts the geometry of both visible and occluded traversable surfaces from input images. The model may be trained from stereo video sequences, using camera poses, per-frame depth, and semantic segmentation to form training data, which is used to supervise an image to image network. In various embodiments, the model is applied to a single RGB image depicting a scene to produce information describing traversable space of the scene that includes occluded traversable. The information describing traversable space can include a segmentation mask of traversable space (both visible and occluded) and non-traversable space and a depth map indicating an estimated depth to traversable surfaces corresponding to each pixel determined to correspond to traversable space.

Determining traversable space from single images

A method for training a depth estimation model with depth hints is disclosed. For each image pair: for a first image, a depth prediction is determined by the depth estimation model and a depth hint is obtained; the second image is projected onto the first image once to generate a synthetic frame based on the depth prediction and again to generate a hinted synthetic frame based on the depth hint; a primary loss is calculated with the synthetic frame; a hinted loss is calculated with the hinted synthetic frame; and an overall loss is calculated for the image pair based on a per-pixel determination of whether the primary loss or the hinted loss is smaller, wherein if the hinted loss is smaller than the primary loss, then the overall loss includes the primary loss and a supervised depth loss between depth prediction and depth hint. The depth estimation model is trained by minimizing the overall losses for the image pairs.

Growing community of inventors

London, United Kingdom

Michael David Firman