Patents by Inventor Debidatta Dwibedi
Debidatta Dwibedi has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11797860Abstract: Systems and methods for cuboid detection and keypoint localization in images are disclosed. In one aspect, a deep cuboid detector can be used for simultaneous cuboid detection and keypoint localization in monocular images. The deep cuboid detector can include a plurality of convolutional layers and non-convolutional layers of a trained convolution neural network for determining a convolutional feature map from an input image. A region proposal network of the deep cuboid detector can determine a bounding box surrounding a cuboid in the image using the convolutional feature map. The pooling layer and regressor layers of the deep cuboid detector can implement iterative feature pooling for determining a refined bounding box and a parameterized representation of the cuboid.Type: GrantFiled: April 11, 2022Date of Patent: October 24, 2023Assignee: MAGIC LEAP, INC.Inventors: Tomasz Jan Malisiewicz, Andrew Rabinovich, Vijay Badrinarayanan, Debidatta Dwibedi
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Publication number: 20230274548Abstract: Techniques are disclosed that enable processing a video capturing a periodic activity using a repetition network to generate periodic output (e.g., a period length of the periodic activity captured in the video and/or a frame wise periodicity indication of the video capturing the periodic activity). Various implementations include a class agnostic repetition network which can be used to generate periodic output for a wide variety of periodic activities. Additional or alternative implementations include generating synthetic repetition videos which can be utilized to train the repetition network.Type: ApplicationFiled: June 10, 2020Publication date: August 31, 2023Inventors: Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Andrew Zisserman, Pierre Sermanet
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Publication number: 20220237815Abstract: Systems and methods for cuboid detection and keypoint localization in images are disclosed. In one aspect, a deep cuboid detector can be used for simultaneous cuboid detection and keypoint localization in monocular images. The deep cuboid detector can include a plurality of convolutional layers and non-convolutional layers of a trained convolution neural network for determining a convolutional feature map from an input image. A region proposal network of the deep cuboid detector can determine a bounding box surrounding a cuboid in the image using the convolutional feature map. The pooling layer and regressor layers of the deep cuboid detector can implement iterative feature pooling for determining a refined bounding box and a parameterized representation of the cuboid.Type: ApplicationFiled: April 11, 2022Publication date: July 28, 2022Inventors: Tomasz Jan Malisiewicz, Andrew Rabinovich, Vijay Badrinarayanan, Debidatta Dwibedi
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Patent number: 11328443Abstract: Systems and methods for cuboid detection and keypoint localization in images are disclosed. In one aspect, a deep cuboid detector can be used for simultaneous cuboid detection and keypoint localization in monocular images. The deep cuboid detector can include a plurality of convolutional layers and non-convolutional layers of a trained convolution neural network for determining a convolutional feature map from an input image. A region proposal network of the deep cuboid detector can determine a bounding box surrounding a cuboid in the image using the convolutional feature map. The pooling layer and regressor layers of the deep cuboid detector can implement iterative feature pooling for determining a refined bounding box and a parameterized representation of the cuboid.Type: GrantFiled: January 12, 2021Date of Patent: May 10, 2022Assignee: Magic Leap, Inc.Inventors: Tomasz Jan Malisiewicz, Andrew Rabinovich, Vijay Badrinarayanan, Debidatta Dwibedi
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Publication number: 20220004883Abstract: An encoder neural network is described which can encode a data item, such as a frame of a video, to form a respective encoded data item. Data items of a first data sequence are associated with respective data items of a second sequence, by determining which of the encoded data items of the second sequence is closest to the encoded data item produced from each data item of the first sequence. Thus, the two data sequences are aligned. The encoder neural network is trained automatically using a training set of data sequences, by an iterative process of successively increasing cycle consistency between pairs of the data sequences.Type: ApplicationFiled: November 21, 2019Publication date: January 6, 2022Inventors: Yusuf Aytar, Debidatta Dwibedi, Andrew Zisserman, Jonathan Tompson, Pierre Sermanet
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Publication number: 20210134000Abstract: Systems and methods for cuboid detection and keypoint localization in images are disclosed. In one aspect, a deep cuboid detector can be used for simultaneous cuboid detection and keypoint localization in monocular images. The deep cuboid detector can include a plurality of convolutional layers and non-convolutional layers of a trained convolution neural network for determining a convolutional feature map from an input image. A region proposal network of the deep cuboid detector can determine a bounding box surrounding a cuboid in the image using the convolutional feature map. The pooling layer and regressor layers of the deep cuboid detector can implement iterative feature pooling for determining a refined bounding box and a parameterized representation of the cuboid.Type: ApplicationFiled: January 12, 2021Publication date: May 6, 2021Inventors: Tomasz Jan Malisiewicz, Andrew Rabinovich, Vijay Badrinarayanan, Debidatta Dwibedi
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Patent number: 10937188Abstract: Systems and methods for cuboid detection and keypoint localization in images are disclosed. In one aspect, a deep cuboid detector can be used for simultaneous cuboid detection and keypoint localization in monocular images. The deep cuboid detector can include a plurality of convolutional layers and non-convolutional layers of a trained convolution neural network for determining a convolutional feature map from an input image. A region proposal network of the deep cuboid detector can determine a bounding box surrounding a cuboid in the image using the convolutional feature map. The pooling layer and regressor layers of the deep cuboid detector can implement iterative feature pooling for determining a refined bounding box and a parameterized representation of the cuboid.Type: GrantFiled: March 5, 2020Date of Patent: March 2, 2021Assignee: Magic Leap, Inc.Inventors: Tomasz Jan Malisiewicz, Andrew Rabinovich, Vijay Badrinarayanan, Debidatta Dwibedi
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Publication number: 20200202554Abstract: Systems and methods for cuboid detection and keypoint localization in images are disclosed. In one aspect, a deep cuboid detector can be used for simultaneous cuboid detection and keypoint localization in monocular images. The deep cuboid detector can include a plurality of convolutional layers and non-convolutional layers of a trained convolution neural network for determining a convolutional feature map from an input image. A region proposal network of the deep cuboid detector can determine a bounding box surrounding a cuboid in the image using the convolutional feature map. The pooling layer and regressor layers of the deep cuboid detector can implement iterative feature pooling for determining a refined bounding box and a parameterized representation of the cuboid.Type: ApplicationFiled: March 5, 2020Publication date: June 25, 2020Inventors: Tomasz Jan Malisiewicz, Andrew Rabinovich, Vijay Badrinarayanan, Debidatta Dwibedi
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Patent number: 10621747Abstract: Systems and methods for cuboid detection and keypoint localization in images are disclosed. In one aspect, a deep cuboid detector can be used for simultaneous cuboid detection and keypoint localization in monocular images. The deep cuboid detector can include a plurality of convolutional layers and non-convolutional layers of a trained convolution neural network for determining a convolutional feature map from an input image. A region proposal network of the deep cuboid detector can determine a bounding box surrounding a cuboid in the image using the convolutional feature map. The pooling layer and regressor layers of the deep cuboid detector can implement iterative feature pooling for determining a refined bounding box and a parameterized representation of the cuboid.Type: GrantFiled: November 14, 2017Date of Patent: April 14, 2020Assignee: Magic Leap, Inc.Inventors: Tomasz Jan Malisiewicz, Andrew Rabinovich, Vijay Badrinarayanan, Debidatta Dwibedi
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Publication number: 20180137642Abstract: Systems and methods for cuboid detection and keypoint localization in images are disclosed. In one aspect, a deep cuboid detector can be used for simultaneous cuboid detection and keypoint localization in monocular images. The deep cuboid detector can include a plurality of convolutional layers and non-convolutional layers of a trained convolution neural network for determining a convolutional feature map from an input image. A region proposal network of the deep cuboid detector can determine a bounding box surrounding a cuboid in the image using the convolutional feature map. The pooling layer and regressor layers of the deep cuboid detector can implement iterative feature pooling for determining a refined bounding box and a parameterized representation of the cuboid.Type: ApplicationFiled: November 14, 2017Publication date: May 17, 2018Inventors: Tomasz Malisiewicz, Andrew Rabinovich, Vijay Badrinarayanan, Debidatta Dwibedi