Toyota Research Institute, Inc.

Leland Stanford Junior University

Toyota Jidosha Kabushiki Kaisha

Toyota Technological Institute at Chicago

University of California

Massachusetts Institute of Technology

Woven Planet North America, Inc.

Adrien David Gaidon

Vitor Guizilini

Rares A Ambrus

Sudeep Pillai

Jie Li

Igor Vasiljevic

Kuan-Hui Lee

Dennis Park

Sergey Zakharov

Jiexiong Tang

Gregory Shakhnarovich

Arjun Bhargava

Wadim Kehl

Leonidas John Guibas

Blake Warren Wulfe

Hanme Kim

Vitor Campagnolo Guizilini

Ehsan Adeli-Mosabbeb

Mac Schwager

Boris Ivanovic

Karttikeya Mangalam

Victor Vaquero Gomez

Jiading Fang

Matthew R Walter

Pavel V Tokmakov

Guy Rosman

Juan Carlos Niebles Duque

Marco Pavone

German Ros Sanchez

Or Litany

Wolfram Burgard

Jia-En M Pan

Juan Carlos Niebles

Negar Zahedi Mehr

Rui Hou

Haruki Nishimura

Felipe Codevilla

Eder Santana

James Joseph Kuffner

Alexander Alspach

Sergey Levine

Kun-Hsin Chen

Nikolaos Michalakis

Naveen Suresh Kuppuswamy

Andrew M Beaulieu

Fredo Durand

Matthew T Kliemann

Logan Michael Ellis

Takaaki Tagawa

Haofeng Chen

Xipeng Wang

Kuan-hui Lee

Davis Rempe

Bertrand Douillard

Vincent Sitzmann

Colin Wei

Jeff Z Haochen

Tengyu Ma

Dian Chen

Allan Ricardo Raventos Knohr

Rowan McAllister

German Ros

Rowan Thomas Mcallister

Jean Mercat

Allan Jabri

Shyamal D Buch

Joshua Tenenbaum

Jiajun Wu

Allan Raventos

Ehsan Adeli Mosabbeb

Amine Elhafsi

Jeff Walls, Sr

Haoye Cai

Takayuki Kanai

Yue Wang

Boxiao Pan

Tolga Birdal

Nishant Rai

Greg Shakhnarovich

De-An Huang

Yu Yao

Andreas Buehler

Mingyu Wang

Kuan Lee

Bingbin Liu

Zhangjie Cao

Xiangru Huang

Justin Solomon

Colton Stearns

Systems and methods described herein relate to self-supervised scale-aware learning of camera extrinsic parameters. One embodiment processes instantaneous velocity between a target image and a context image captured by a first camera; jointly training a depth network and pose network based on scaling by the instantaneous velocity; produce depth map using the depth network; produce ego-motion of the first camera using the pose network; generate synthesized image from the target image using a reprojection operation based on the depth map, the ego-motion, the context image and camera intrinsics; determine photometric loss by comparing the synthesized image to the target image; generate photometric consistency constraint using a gradient from the photometric loss; determine pose consistency constraint between the first camera and a second camera; and optimize the photometric consistency constraint, the pose consistency constraint, the depth network and the pose network to generate estimated extrinsic parameters.

Self extrinsic self-calibration via geometrically consistent self-supervised depth and ego-motion learning

A method of generating additional supervision data to improve learning of a geometrically-consistent latent scene representation with a geometric scene representation architecture is provided. The method includes receiving, with a computing device, a latent scene representation encoding a pointcloud from images of a scene captured by a plurality of cameras each with known intrinsics and poses, generating a virtual camera having a viewpoint different from viewpoints of the plurality of cameras, projecting information from the pointcloud onto the viewpoint of the virtual camera, and decoding the latent scene representation based on the virtual camera thereby generating an RGB image and depth map corresponding to the viewpoint of the virtual camera for implementation as additional supervision data.

Geometric 3D augmentations for transformer architectures

A method for multiple object tracking includes receiving, with a computing device, a point cloud dataset, detecting one or more objects in the point cloud dataset, each of the detected one or more objects defined by points of the point cloud dataset and a bounding box, querying one or more historical tracklets for historical tracklet states corresponding to each of the one or more detected objects, implementing a 4D encoding backbone comprising two branches: a first branch configured to compute per-point features for each of the one or more objects and the corresponding historical tracklet states, and a second branch configured to obtain 4D point features, concatenating the per-point features and the 4D point features, and predicting, with a decoder receiving the concatenated per-point features, current tracklet states for each of the one or more objects.

Systems and methods for multi-object tracking

Systems and methods described herein relate to dynamics-aware comparison of reward functions. One embodiment generates a reference reward function; computes a dynamics-aware transformation of the reference reward function based on a transition model of an environment of a robot; computes a dynamics-aware transformation of a first candidate reward function based on the transition model; computes a dynamics-aware transformation of a second candidate reward function based on the transition model; selects, as a final reward function, the first or second candidate reward function based on which is closer to the reference reward function as measured by pseudometrics computed between their respective dynamics-aware transformations and the dynamics-aware transformation of the reference reward function; and optimizes the final reward function to control, at least in part, operation of the robot.

Systems and methods for dynamics-aware comparison of reward functions

Systems and methods for enhanced computer vision capabilities, particularly including depth synthesis, which may be applicable to autonomous vehicle operation are described. A vehicle may be equipped with a geometric scene representation (GSR) architecture for synthesizing depth views at arbitrary viewpoints. The GSR architecture synthesizes depth views enable advanced functions, including depth interpolation and depth extrapolation. The GSR architecture implements functions (i.e., depth interpolation, depth extrapolation) that are useful for various computer vision applications for autonomous vehicles, such as predicting depth maps from unseen locations. For example, a vehicle includes a processor device synthesizing depth views at multiple viewpoints, where the multiple viewpoints are from image data of a surrounding environment for the vehicle. Further, the vehicle can have a controller device that receives depth views from the processor device and performs autonomous operations in response to analysis of the depth views.

Systems and methods for depth synthesis with transformer architectures

A method for self-supervised learning is described. The method includes generating a plurality of augmented data from unlabeled image data. The method also includes generating a population augmentation graph for a class determined from the plurality of augmented data. The method further includes minimizing a contrastive loss based on a spectral decomposition of the population augmentation graph to learn representations of the unlabeled image data. The method also includes classifying the learned representations of the unlabeled image data to recover ground-truth labels of the unlabeled image data.

Provable guarantees for self-supervised deep learning with spectral contrastive loss

A method of representation learning for object detection from unlabeled point cloud sequences is described. The method includes detecting moving object traces from temporally-ordered, unlabeled point cloud sequences. The method also includes extracting a set of moving objects based on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences. The method further includes classifying the set of moving objects extracted from on the moving object traces detected from the sequence of temporally-ordered, unlabeled point cloud sequences. The method also includes estimating 3D bounding boxes for the set of moving objects based on the classifying of the set of moving objects.

Representation learning for object detection from unlabeled point cloud sequences

A method for improving 3D object detection via object-level augmentations is described. The method includes recognizing, using an image recognition model of a differentiable data generation pipeline, an object in an image of a scene. The method also includes generating, using a 3D reconstruction model, a 3D reconstruction of the scene from the image including the recognized object. The method further includes manipulating, using an object level augmentation model, a random property of the object by a random magnitude at an object level to determine a set of properties and a set of magnitudes of an object manipulation that maximizes a loss function of the image recognition model. The method also includes training a downstream task network based on a set of training data generated based on the set of properties and the set of magnitudes of the object manipulation, such that the loss function is minimized.

Adversarial object-aware neural scene rendering for 3D object detection

A method includes receiving first driving data associated with a first vehicle, receiving second driving data associated with one or more vehicles around the first vehicle, creating training data by labeling the first driving data as positive data and treating the second driving data as unlabeled, and using the training data to train a classifier to predict whether driving data input to the classifier is positive or unlabeled.

Method and system for learning reward functions for driving using positive-unlabeled reward learning

Systems and methods for self-supervised learning for visual odometry using camera images, may include: estimating correspondences between keypoints of a target camera image and keypoints of a context camera image; based on the keypoint correspondences, lifting a set of 2D keypoints to 3D, using a neural camera model; and projecting the 3D keypoints into the context camera image using the neural camera model. Some embodiments may use the neural camera model to achieve the lifting and projecting of keypoints without a known or calibrated camera model.

Systems and methods for generic visual odometry using learned features via neural camera models

A method for 3D object detection is described. The method includes concurrently training a monocular depth network and a 3D object detection network. The method also includes predicting, using a trained monocular depth network, a monocular depth map of a monocular image of a video stream. The method further includes inferring a 3D point cloud of a 3D object within the monocular image according to the predicted monocular depth map. The method also includes predicting 3D bounding boxes from a selection of 3D points from the 3D point cloud of the 3D object based on a selection regression loss.

Monocular object detection via end-to-end differentiable pipeline

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.

Self-supervised monocular depth estimation via rigid-motion embeddings

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.

System and method for self-supervised monocular ground-plane extraction

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.

Systems and methods for estimating scaled maps by sampling representations from a learning model

A method for depth estimation performed by a depth estimation system associated with an agent includes determining a first depth of a first image and a second depth of a second image, the first image and the second image being captured by a sensor associated with the agent. The method also includes generating a first 3D image of the first image based on the first depth, a first pose associated with the sensor, and the second image. The method further includes generating a warped depth image based on transforming the first depth in accordance with the first pose. The method also includes updating the first pose based on a second pose associated with the warped depth image and the second depth, and updating the first 3D image based on the updated first pose. The method further includes controlling an action of the agent based on the updated first 3D image.

Depth estimation based on ego-motion estimation and residual flow estimation

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Mountain View, CA, United States of America

Adrien David Gaidon