Robert Boschgmbh

Yuliang Guo

Liu Ren

Xinyu Huang

Liang Gou

Yuyan Li

Yiqi Zhong

Zhixin Yan

A system and method are disclosed herein for developing a machine perception model in the omnidirectional image domain. The system and method utilize the knowledge distillation process to transfer and adapt knowledge from the perspective projection image domain to the omnidirectional image domain. A teacher model is pre-trained to perform the machine perception task in the perspective projection image. A student model is trained by adapting the pre-existing knowledge of the teacher model from the perspective projection image domain to the omnidirectional image domain. By way of this training, the student model learns to perform the same machine perception task, except in the omnidirectional image domain, using limited or no suitably labeled training data in the omnidirectional image domain.

Method for fast domain adaptation from perspective projection image domain to omnidirectional image domain in machine perception tasks

A method for performing at least one perception task associated with autonomous vehicle control includes receiving a first dataset and identifying a first object category of objects associated with the plurality of images, the first object category including a plurality of object types. The method also includes identifying a current statistical distribution of a first object type of the plurality of object types and determining a first distribution difference between the current statistical distribution of the first object type and a standard statistical distribution associated with the first object category. The method also includes, in response to a determination that the first distribution difference is greater than a threshold, generating first object type data corresponding to the first object type, configuring at least one attribute of the first object type data, and generating a second dataset by augmenting the first dataset using the first object type data.

Systems and methods for multi-modal data augmentation for perception tasks in autonomous driving

A method and system for an augmented reality assistant that recognizes a step in a product assembly process and assists in the installation of a constituent component into a base component. That system having a prepopulated database of templates, the templates being generated based off of two-dimensional images and the related three-dimensional models. The template database is used to train a first machine learning model, that model configured to identify the step in the product assembly process based on an image captured from an image capture device. Verifying that determination by a second machine learning model. Presenting an AR assistant to the user to assist with that step based on the related template.

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Palo Alto, CA, United States of America

Yuliang Guo