Abstract: Systems and techniques are provided for generating depth information. For example, a process can include obtaining a first feature volume including visual features corresponding to each respective frame included in a first set of frames. A first query generator network can generate reconstruction features associated with a reconstructed feature volume corresponding to the first feature volume. Based on the first feature volume, a second query generator network can generate motion features associated with predicted future motion corresponding to the first feature volume. An initial depth prediction can be generated for each respective frame based on cross-attention between features of a depth prediction decoder, the reconstruction features, and the motion features. A refined depth prediction can be generated for each respective based on cross-attention between the initial depth prediction, the reconstruction features, and the motion features.

Abstract: Systems and techniques are provided for generating depth information from one or more images. For example, a process can include obtaining a first depth map corresponding to an input comprising an image of the one or more images and a sparse depth measurement. A three-dimensional (3D) point cloud can be generated based on the first depth map and multi-scale visual features of the input, wherein the 3D point cloud includes a plurality of 3D point features uplifted from the multi-scale visual features. At least a portion of the plurality of 3D point features can be processed using one or more self-attention layers to generate refined 3D point features. A two-dimensional (2D) projection of the refined 3D point features can be generated and a second depth map can be generated based on the 2D projection of the refined 3D point features.

Abstract: A processor-implemented method for attention-based depth completion includes receiving, by an artificial neural network (ANN), an input. The input includes an image and a sparse depth measurement. The ANN extracts multi-scale visual features of the input. The ANN applies a self-attention mechanism to the multi-scale visual features to generate a set of attended multi-scale visual features. The ANN generates a dense depth map based on the set of attended multi-scale visual features.

Abstract: Systems and techniques are provided for processing image data. According to some aspects, a computing device can generate a gradient (e.g., a classifier gradient using a trained classifier) associated with a current sample. The computing device can combine the gradient with an iterative model estimated score function or data associated with the current sample to generate a score function estimate. The computing device can predict, using the diffusion machine learning model and based on the score function estimate, a new sample.

Abstract: An example device for processing image data includes a memory configured to store image data; and a processing system comprising one or more processors implemented in circuitry, the processing system being configured to: obtain an image to be processed; obtain a first auxiliary value for the image and a second auxiliary value for the image; generate an input component including the first auxiliary value and the second auxiliary value arranged in a pattern according to a stride of a neural network; and provide the image and the input component to the neural network.

Abstract: Example systems and techniques are described for controlling operation of a vehicle and training a machine learning model for controlling operation of a vehicle. A system includes memory configured to store point cloud data associated with the vehicle and one or more processors communicatively coupled to the memory. The one or more processors are configured to determine a depth map indicative of distance of one or more objects to the vehicle and control operation of a vehicle based on the depth map. The depth map is based on executing a machine learning model, the machine learning model being trained with a slice loss function determined from training point cloud data having a respective depth that is greater than the average depth for a set of points of the point cloud data plus a threshold.

Abstract: An apparatus, method and computer-readable media are disclosed for processing images. For example, a method is provided for processing images for one or more visual perception tasks using a machine learning system including one or more transformer layers. The method includes: obtaining a plurality of input images associated with a plurality of spatial views of a scene; generating, using a machine learning-based encoder of a machine learning system, a plurality of features from the plurality of input images; and combining timing information associated with capture of the plurality of input images with at least one input of the machine learning system to synchronize the plurality of features in time.

Abstract: An example device for processing image data includes a processing unit configured to: receive, from a camera of a vehicle, a first image frame at a first time and a second image frame at a second time; receive, from an odometry unit of the vehicle, a first position of the vehicle at the first time and a second position of the vehicle at a second time; calculate a pose difference value representing a difference between the second and first positions; form a pose frame having a size corresponding to the first and second image frames and sample values including the pose difference value; and provide the first and second image frames and the pose frame to a neural networking unit configured to calculate depth for objects in the first image frame and the second image frame, the depth for the objects representing distances between the objects and the vehicle.

Abstract: This disclosure provides systems, methods, and devices for image signal processing that support multi-source pose merging for depth estimation. In a first aspect, a method of image processing includes generating, in accordance with first image data of a first image frame and second image data of a second image frame, a first mask indicating one or more pixels determined not to change position between the first image frame and the second image frame, generating, in accordance with the first image data and the second image data, a second mask indicating one or more pixels determined not to change position between the first image frame and the second image frame, and combining the first mask with the second mask to generate a third mask. Other aspects and features are also claimed and described.

Abstract: Disclosed are systems and techniques for capturing images (e.g., using a monocular image sensor) and detecting depth information. According to some aspects, a computing system or device can generate a feature representation of a current image and update accumulated feature information for storage in a memory based on a feature representation of a previous image and optical flow information of the previous image. The accumulated feature information can include accumulated image feature information associated with a plurality of previous images and accumulated optical flow information associated of the plurality of previous images. The computing system or device can obtain information associated with relative motion of the current image based on the accumulated feature information and the feature representation of the current image.

Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a computing device receives a predicted depth map determined by a model and determines differences between the predicted depth map and a depth mask. The depth mask may be predetermined to include values indicating a probability that a corresponding region of the predicted depth map is a sky region. A first loss term for the predicted depth map is determined based on the differences and the model is trained based on the first loss term. Other aspects and features are also claimed and described.

Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a computing device may receive a predicted depth map and a measured depth map and may determine a time difference between the predicted depth map and the measured depth map. A supervision loss term may be determined based on the time difference, such as by weighting the supervision loss term based on the time difference. The computing device may train a model based on the supervision loss term, such as a model that generated the predicted depth map. Other aspects and features are also claimed and described.

Abstract: Systems and techniques are provided for processing sensor data. For example, a process can include determining, using a trained machine learning system, a predicted depth map for an image, the predicted depth map including a respective predicted depth value for each pixel of the image. The process can further include obtaining depth values for the image, the depth values including depth values for less than all pixels of the image from a tracker configured to determine the depth values based on one or more feature points between frames. The process can further include scaling the predicted depth map for the image using and the depth values. The output of the process can be scale-correct depth prediction values.

Abstract: Systems and techniques are provided for processing image data. For example, a process can include obtaining a plurality of input images associated with a plurality of different spatial views. The process can include generating a set of features based on the plurality of input images. The process can include generating a set of projected features based on the set of features, wherein an embedding size associated with the set of projected features is smaller than an embedding size associated with the set of features. The process can include determining a cross-view attention associated with the plurality of different spatial views, the cross-view attention determined using the set of projected features.

Abstract: A brake cylinder dust cover is installed on a waterproof brake cylinder. The brake cylinder includes a brake cylinder body and a front cover. A waterproof dust cover assembly is arranged on the front cover. The waterproof dust cover assembly includes a dust cover fixedly connected to the front cover, and a filter felt support and a filter felt pad which are arranged in the dust cover. The filter felt support is connected with the dust cover, and the filter felt pad is fixed on the filter felt support. An outwards extending skirt is arranged at the edge of the dust cover in the axial direction thereof.

Abstract: The invention discloses a brake cylinder dust cover and a waterproof brake cylinder using such dust cover. The brake cylinder comprises a brake cylinder body and a front cover. A waterproof dust cover assembly is arranged on the front cover. The waterproof dust cover assembly comprises a dust cover fixedly connected to the front cover, and a filter felt support and a filter felt pad which are arranged in the dust cover. The filter felt support is connected with the dust cover, and the filter felt pad is fixed on the filter felt support. An outwards extending skirt is arranged at the edge of the dust cover in the axial direction thereof.