Abstract: In accordance with one embodiment of the present disclosure, a method includes receiving a set of images, each image depicting a view of a scene, generating sparse depth data from each image of the set of images, training a monocular depth estimation model with the sparse depth data, generating, with the trained monocular depth estimation model, depth data and uncertainty data for each image, training a NeRF model with the set of images, wherein the training is constrained by the depth data and uncertainty data, and rendering, with the trained NeRF model, a new image having a new view of the scene.

Abstract: Systems and methods described herein relate to training a machine-learning-based monocular depth estimator.

Abstract: Described are systems and methods for self-learned label refinement of a training set. In on example, a system includes a processor and a memory having a training set generation module that causes the processor to train a model using an image as an input to the model and 2D bounding based on 3D bounding boxes as ground truths, select a first subset from predicted 2D bounding boxes previously outputted by the model, retrain the model using the image as the input and the first subset as ground truths, select a second set of predicted 2D bounding boxes previously outputted by the model, and generate the training set by selecting the 3D bounding boxes from a master set of 3D bounding boxes that have corresponding 2D bounding boxes that form the second subset.

Abstract: System, methods, and other embodiments described herein relate to training a depth model for monocular depth estimation using photometric loss masks derived from motion estimates of dynamic objects. In one embodiment, a method includes generating depth maps from images of an environment. The method includes determining motion of points within the depth maps. The method includes associating the points between the depth maps to identify an object according to a correlation of the motion for a first cluster of the points with a second cluster of the points. The method includes providing the depth maps and the object as an electronic output.

Abstract: A method for controlling an agent to navigate through an environment includes generating a depth map associated with a monocular image of the environment. The method also includes generating a group of surface normal. Each surface normal of the group of surface normals is associated with a respective polygon of a group of polygons associated with the depth map. The method further includes identifying one or more ground planes in the depth map based on the group of surface normal. The method further includes controlling the agent to navigate through the environment based on identifying the one or more ground planes.

Abstract: A representation of a spatial structure of objects in an image can be determined. A mode of a neural network can be set, in response to a receipt of the image and a receipt of a facing direction of a camera that produced the image. The mode can account for the facing direction. The facing direction can include one or more of a first facing direction of a first camera disposed on a vehicle or a second facing direction of a second camera disposed on the vehicle. The neural network can be executed, in response to the mode having been set, to determine the representation of the spatial structure of the objects in the image. The representation of the spatial structure of the objects in the image can be transmitted to an automotive navigation system to determine a distance between the vehicle and a specific object in the image.

Abstract: Systems and methods for tracking objects are disclosed herein. In one embodiment, a system having a processor merges features of detected objects extracted from a point cloud and a corresponding image to generate fused features for the detected objects, generates a learned distance metric for the detected objects using the fused features, determines matched detected objects and unmatched detected objects, applies prior tracking identifiers of the detected objects at the prior time to the matched detected objects, determines a confidence score for the fused features of the unmatched detected objects, and applies new tracking identifiers to the unmatched detected objects based on the confidence score.

Abstract: Systems and methods for extracting ground plane information directly from monocular images using self-supervised depth networks are disclosed. Self-supervised depth networks are used to generate a three-dimensional reconstruction of observed structures. From this reconstruction the system may generate surface normals. The surface normals can be calculated directly from depth maps in a way that is much less computationally expensive and accurate than surface normals extraction from standard LiDAR data. Surface normals facing substantially the same direction and facing upwards may be determined to reflect a ground plane.

Abstract: Systems and methods for self-supervised depth estimation using image frames captured from a camera mounted on a vehicle comprise: receiving a first image from the camera mounted at a first location on the vehicle; receiving a second image from the camera mounted at a second location on the vehicle; predicting a depth map for the first image; warping the first image to a perspective of the camera mounted at the second location on the vehicle to arrive at a warped first image; projecting the warped first image onto the second image; determining a loss based on the projection; and updating the predicted depth values for the first image.

Abstract: A method for monocular depth/pose estimation in a camera agnostic network is described. The method includes projecting lifted 3D points onto an image plane according to a predicted ray vector based on a monocular depth model, a monocular pose model, and a camera center of a camera agnostic network. The method also includes predicting a warped target image from a predicted depth map of the monocular depth model, a ray surface of the predicted ray vector, and a projection of the lifted 3D points according to the camera agnostic network.

Abstract: System, methods, and other embodiments described herein relate to determining depths of a scene from a monocular image. In one embodiment, a method includes generating depth features from sensor data according to whether the sensor data includes sparse depth data. The method includes selectively injecting the depth features into a depth model. The method includes generating a depth map from at least a monocular image using the depth model that is guided by the depth features when injected. The method includes providing the depth map as depth estimates of objects represented in the monocular image.

Abstract: System, methods, and other embodiments described herein relate to determining depths of a scene from a monocular image. In one embodiment, a method includes generating depth features from depth data using a sparse auxiliary network (SAN) by i) sparsifying the depth data, ii) applying sparse residual blocks of the SAN to the depth data, and iii) densifying the depth features. The method includes generating a depth map from the depth features and a monocular image that corresponds with the depth data according to a depth model that includes the SAN. The method includes providing the depth map as depth estimates of objects represented in the monocular image.

Abstract: In accordance with one embodiment of the present disclosure, a method includes receiving an input image having an object and a background, intrinsically decomposing the object and the background into an input image data having a set of features, augmenting the input image data with a 2.5D differentiable renderer for each feature of the set of features to create a set of augmented images, and compiling the input image and the set of augmented images into a training data set for training a downstream task network.

Abstract: A system for producing a depth map can include a processor and a memory. The memory can store a neural network. The neural network can include an encoding portion module, a multi-frame feature matching portion module, and a decoding portion module. The encoding portion module can include instructions that, when executed by the processor, cause the processor to encode an image to produce single-frame features. The multi-frame feature matching portion module can include instructions that, when executed by the processor, cause the processor to process the single-frame features to produce information. The decoding portion module can include instructions that, when executed by the processor, cause the processor to decode the information to produce the depth map. A first training dataset, used to train the multi-frame feature matching portion module, can be different from a second training dataset used to train the encoding portion module and the decoding portion module.

Abstract: A system for producing a depth map can include a processor and a memory. The memory can store a candidate depth production module and a depth map production module. The candidate depth production module can include instructions that cause the processor to: (1) identify, in a first image, an epipolar line associated with a pixel in a second image and (2) sample, from a first image feature set, a set of candidate depths for pixels along the epipolar line. The depth map production module can include instructions that cause the processor to: (1) determine a similarity measure between a feature, from a second image feature set, and a member of the set and (2) produce, from the second image, the depth map with a depth for the pixel being a depth associated with a member, of the set, having a greatest similarity measure.

Abstract: System, methods, and other embodiments described herein relate to improving depth estimates for monocular images using a neural camera model that is independent of a camera type. In one embodiment, a method includes receiving a monocular image from a pair of training images derived from a monocular video. The method includes generating, using a ray surface network, a ray surface that approximates an image character of the monocular image as produced by a camera having the camera type. The method includes creating a synthesized image according to at least the ray surface and a depth map associated with the monocular image.

Abstract: A method for behavior cloned vehicle trajectory planning is described. The method includes perceiving vehicles proximate an ego vehicle in a driving environment, including a scalar confidence value of each perceived vehicle. The method also includes generating a bird's-eye-view (BEV) grid showing the ego vehicle and each perceived vehicle based on each of the scalar confidence values. The method further includes ignoring at least one of the perceived vehicles when the scalar confidence value of the at least one of the perceived vehicles is less than a predetermined value. The method also includes selecting an ego vehicle trajectory based on a cloned expert vehicle behavior policy according to remaining perceived vehicles.

Abstract: Systems and methods described herein relate to training a machine-learning-based monocular depth estimator. One embodiment selects a virtual image in a virtual dataset, the virtual dataset including a plurality of computer-generated virtual images; generates, from the virtual image in accordance with virtual-camera intrinsics, a point cloud in three-dimensional space based on ground-truth depth information associated with the virtual image; reprojects the point cloud back to two-dimensional image space in accordance with real-world camera intrinsics to generate a transformed virtual image; and trains the machine-learning-based monocular depth estimator, at least in part, using the transformed virtual image.

Abstract: A method for tracking an object performed by an object tracking system includes encoding locations of visible objects in an environment captured in a current frame of a sequence of frames. The method also includes generating a representation of a current state of the environment based on an aggregation of the encoded locations and an encoded location of each object visible in one or more frames of the sequence of frames occurring prior to the current frame. The method further includes predicting a location of an object occluded in the current frame based on a comparison of object centers decoded from the representation of the current state to object centers saved from each prior representation associated with a different respective frame of the sequence of frames occurring prior to the current frame. The method still further includes adjusting a behavior of an autonomous agent in response to identifying the location of the occluded object.

Abstract: Systems and methods for map construction using a video sequence captured on a camera of a vehicle in an environment, comprising: receiving a video sequence from the camera, the video sequence including a plurality of image frames capturing a scene of the environment of the vehicle; using a neural camera model to predict a depth map and a ray surface for the plurality of image frames in the received video sequence; and constructing a map of the scene of the environment based on image data captured in the plurality of frames and depth information in the predicted depth maps.