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1.发明公开公开(公告)号：US20240354991A1
公开(公告)日：2024-10-24
申请号：US18486619
申请日：2023-10-13
发明人： Vitor Campagnolo Guizilini , Igor Vasiljevic , Dian Chen , Adrien David Gaidon , Rares A. Ambrus
CPC分类号： G06T7/80 , G06T7/50 , G06T2207/10028 , G06T2207/20081
摘要： Systems, methods, and other embodiments described herein relate to estimating scaled depth maps by sampling variational representations of an image using a learning model. In one embodiment, a method includes encoding data embeddings by a learning model to form conditioned latent representations using attention networks, the data embeddings including features about an image from a camera and calibration information about the camera. The method also includes computing a probability distribution of the conditioned latent representations by factoring scale priors. The method also includes sampling the probability distribution to generate variations for the data embeddings. The method also includes estimating scaled depth maps of a scene from the variations at different coordinates using the attention networks.
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2.发明授权公开(公告)号：US12039438B2
公开(公告)日：2024-07-16
申请号：US17112292
申请日：2020-12-04
发明人： Boris Ivanovic , Kuan-Hui Lee , Jie Li , Adrien David Gaidon , Pavel Tokmakov
CPC分类号： G06N3/08 , B60W30/0956 , G06N3/044 , B60W60/0027 , B60W2554/4044 , G05D1/0214
摘要： Systems, methods, and other embodiments described herein relate to improving trajectory forecasting in a device. In one embodiment, a method includes, in response to receiving sensor data about a surrounding environment of the device, identifying an object from the sensor data that is present in the surrounding environment. The method includes determining category probabilities for the object, the category probabilities indicating semantic classes for classifying the object and probabilities that the object belongs to the semantic classes. The method includes forecasting trajectories for the object based, at least in part, on the category probabilities and the sensor data. The method includes controlling the device according to the trajectories.
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公开(公告)号：US11967141B2
公开(公告)日：2024-04-23
申请号：US18161777
申请日：2023-01-30
发明人： Adrien David Gaidon , Jie Li
IPC分类号： G06N3/04 , G06F17/18 , G06F18/20 , G06F18/2113 , G06F18/25 , G06N3/045 , G06V10/764 , G06V10/771 , G06V10/80 , G06V10/82 , G06V20/56 , G06V40/10
CPC分类号： G06V10/82 , G06F17/18 , G06F18/2113 , G06F18/25 , G06F18/29 , G06N3/045 , G06V10/764 , G06V10/771 , G06V10/80 , G06V20/56 , G06V40/10
摘要： One or more embodiments of the present disclosure include systems and methods that use neural architecture fusion to learn how to combine multiple separate pre-trained networks by fusing their architectures into a single network for better computational efficiency and higher accuracy. For example, a computer implemented method of the disclosure includes obtaining multiple trained networks. Each of the trained networks may be associated with a respective task and has a respective architecture. The method further includes generating a directed acyclic graph that represents at least a partial union of the architectures of the trained networks. The method additionally includes defining a joint objective for the directed acyclic graph that combines a performance term and a distillation term. The method also includes optimizing the joint objective over the directed acyclic graph.
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公开(公告)号：US11948310B2
公开(公告)日：2024-04-02
申请号：US17489237
申请日：2021-09-29
CPC分类号： G06T7/248 , G05D1/0221 , G05D1/0246 , G06N3/045 , G06N3/08 , G06T7/50 , G06T7/55 , G06T7/73 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084
摘要： Systems and methods described herein relate to jointly training a machine-learning-based monocular optical flow, depth, and scene flow estimator. One embodiment processes a pair of temporally adjacent monocular image frames using a first neural network structure to produce a first optical flow estimate; processes the pair of temporally adjacent monocular image frames using a second neural network structure to produce an estimated depth map and an estimated scene flow; processes the estimated depth map and the estimated scene flow using the second neural network structure to produce a second optical flow estimate; and imposes a consistency loss between the first optical flow estimate and the second optical flow estimate that minimizes a difference between the first optical flow estimate and the second optical flow estimate to improve performance of the first neural network structure in estimating optical flow and the second neural network structure in estimating depth and scene flow.
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公开(公告)号：US11915487B2
公开(公告)日：2024-02-27
申请号：US16867124
申请日：2020-05-05
IPC分类号： G06V20/56 , G06N3/08 , G06T7/50 , G06F18/214 , G06V10/764 , G06V10/82
CPC分类号： G06V20/56 , G06F18/214 , G06N3/08 , G06T7/50 , G06V10/764 , G06V10/82
摘要： Systems and methods to improve machine learning by explicitly over-fitting environmental data obtained by an imaging system, such as a monocular camera are disclosed. The system includes training self-supervised depth and pose networks in monocular visual data collected from a certain area over multiple passes. Pose and depth networks may be trained by extracting data from multiple images of a single environment or trajectory, allowing the system to overfit the image data.
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公开(公告)号：US11868439B2
公开(公告)日：2024-01-09
申请号：US17215646
申请日：2021-03-29
发明人： Vitor Guizilini , Adrien David Gaidon , Jie Li , Rares A. Ambrus
CPC分类号： G06F18/2178 , G06F18/2148 , G06T7/50 , G06T7/74 , G06T9/002 , G06V20/56 , G06V20/64 , G06T2207/10024 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084
摘要： Systems, methods, and other embodiments described herein relate to training a multi-task network using real and virtual data. In one embodiment, a method includes acquiring training data that includes real data and virtual data for training a multi-task network that performs at least depth prediction and semantic segmentation. The method includes generating a first output from the multi-task network using the real data and second output from the multi-task network using the virtual data. The method includes generating a mixed loss by analyzing the first output to produce a real loss and the second output to produce a virtual loss. The method includes updating the multi-task network using the mixed loss.
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公开(公告)号：US11783593B2
公开(公告)日：2023-10-10
申请号：US17830918
申请日：2022-06-02
IPC分类号： G06V20/56 , G06T7/50 , G06V40/10 , G06V10/75 , G06F18/214
CPC分类号： G06V20/56 , G06F18/214 , G06T7/50 , G06V10/751 , G06V40/10 , G06T2207/10028
摘要： A method for navigating a vehicle through an environment includes assigning a first weight to each pixel associated with a dynamic object and assigning a second weight to each pixel associated with a static object. The method also includes generating a dynamic object depth estimate for the dynamic object and generating a static object depth estimate for the static object, an accuracy of the dynamic object depth estimate being greater than an accuracy of the static object depth estimate. The method still further includes generating a 3D estimate of the environment based on the dynamic object depth estimate and the static object depth estimate. The method also includes controlling an action of the vehicle based on the 3D estimate of the environment.
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8.发明授权公开(公告)号：US11727589B2
公开(公告)日：2023-08-15
申请号：US17377684
申请日：2021-07-16
发明人： Vitor Guizilini , Rares Andrei Ambrus , Adrien David Gaidon , Igor Vasiljevic , Gregory Shakhnarovich
IPC分类号： G06T7/55 , B60R1/00 , G06T3/00 , G05D1/02 , G06N3/08 , G06T7/579 , G06T7/292 , G06T7/11 , B60W60/00 , G06T3/40 , G06F18/214 , H04N23/90
CPC分类号： G06T7/55 , B60R1/00 , B60W60/001 , G05D1/0212 , G05D1/0246 , G06F18/214 , G06F18/2148 , G06N3/08 , G06T3/0012 , G06T3/0093 , G06T3/40 , G06T7/11 , G06T7/292 , G06T7/579 , H04N23/90 , B60R2300/102 , B60W2420/42 , G05D2201/0213 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084 , G06T2207/30244 , G06T2207/30252
摘要： A method for multi-camera monocular depth estimation using pose averaging is described. The method includes determining a multi-camera photometric loss associated with a multi-camera rig of an ego vehicle. The method also includes determining a multi-camera pose consistency constraint (PCC) loss associated with the multi-camera rig of the ego vehicle. The method further includes adjusting the multi-camera photometric loss according to the multi-camera PCC loss to form a multi-camera PCC photometric loss. The method also includes training a multi-camera depth estimation model and an ego-motion estimation model according to the multi-camera PCC photometric loss. The method further includes predicting a 360° point cloud of a scene surrounding the ego vehicle according to the trained multi-camera depth estimation model and the ego-motion estimation model.
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公开(公告)号：US11688090B2
公开(公告)日：2023-06-27
申请号：US17377161
申请日：2021-07-15
发明人： Vitor Guizilini , Rares Andrei Ambrus , Adrien David Gaidon , Igor Vasiljevic , Gregory Shakhnarovich
IPC分类号： G06T7/55 , G06N3/08 , G06T7/579 , B60R1/00 , G06T3/00 , G05D1/02 , G06T7/292 , G06T7/11 , B60W60/00 , G06T3/40 , G06F18/214 , H04N23/90
CPC分类号： G06T7/55 , B60R1/00 , B60W60/001 , G05D1/0212 , G05D1/0246 , G06F18/214 , G06F18/2148 , G06N3/08 , G06T3/0012 , G06T3/0093 , G06T3/40 , G06T7/11 , G06T7/292 , G06T7/579 , H04N23/90 , B60R2300/102 , B60W2420/42 , G05D2201/0213 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084 , G06T2207/30244 , G06T2207/30252
摘要： A method for multi-camera self-supervised depth evaluation is described. The method includes training a self-supervised depth estimation model and an ego-motion estimation model according to a multi-camera photometric loss associated with a multi-camera rig of an ego vehicle. The method also includes generating a single-scale correction factor according to a depth map of each camera of the multi-camera rig during a time-step. The method further includes predicting a 360° point cloud of a scene surrounding the ego vehicle according to the self-supervised depth estimation model and the ego-motion estimation model. The method also includes scaling the 360° point cloud according to the single-scale correction factor to form an aligned 360° point cloud.
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公开(公告)号：US11662731B2
公开(公告)日：2023-05-30
申请号：US17174789
申请日：2021-02-12
申请人： Toyota Research Institute, Inc. , The Board of Trustees of the Leland Stanford Junior University
CPC分类号： G05D1/0214 , G05D1/0287 , B25J9/163
摘要： Systems and methods described herein relate to controlling a robot. One embodiment receives an initial state of the robot, an initial nominal control trajectory of the robot, and a Kullback-Leibler (KL) divergence bound between a modeled probability distribution for a stochastic disturbance and an unknown actual probability distribution for the stochastic disturbance; solves a bilevel optimization problem subject to the modeled probability distribution and the KL divergence bound using an iterative Linear-Exponential-Quadratic-Gaussian (iLEQG) algorithm and a cross-entropy process, the iLEQG algorithm outputting an updated nominal control trajectory, the cross-entropy process outputting a risk-sensitivity parameter; and controls operation of the robot based, at least in part, on the updated nominal control trajectory and the risk-sensitivity parameter.
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