SYSTEM AND METHOD FOR UNKNOWN OBJECT MANIPULATION FROM PURE SYNTHETIC STEREO DATA

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Inventor(s)

Mark Edward Tjersland of Mountain View CA (US)

SYSTEM AND METHOD FOR UNKNOWN OBJECT MANIPULATION FROM PURE SYNTHETIC STEREO DATA - A simplified explanation of the abstract

This abstract first appeared for US patent application 17839193 titled 'SYSTEM AND METHOD FOR UNKNOWN OBJECT MANIPULATION FROM PURE SYNTHETIC STEREO DATA

Simplified Explanation

The abstract describes a method for training a neural network to manipulate 3D objects. Here are the key points:

The method involves using synthetic stereo images to train the neural network.
Features are extracted from each image of the stereo pair.
A low-resolution disparity image is generated based on the extracted features.
The neural network generates a feature map using the low-resolution disparity image and one of the stereo images.
The feature map is used to manipulate an unknown object based on a perception prediction from a prediction head.

Potential Applications

This technology has potential applications in various fields, including:

Robotics: The ability to manipulate 3D objects can be useful in robotic systems for tasks such as object recognition, grasping, and manipulation.
Augmented Reality: The technology can enhance augmented reality applications by enabling realistic and accurate object manipulation in virtual environments.
Computer Vision: The method can be used to improve computer vision systems for tasks like object tracking, scene understanding, and depth estimation.

Problems Solved

The technology addresses several problems in the field of 3D object manipulation, including:

Lack of training data: Synthetic stereo images provide a large amount of training data, which can be used to train the neural network for object manipulation tasks.
Low-resolution disparity estimation: The method generates a low-resolution disparity image, which helps in estimating the depth information of the objects in the scene.
Perception prediction: The prediction head of the neural network provides perception predictions, which can be used to manipulate the objects based on their perceived properties.

Benefits

The technology offers several benefits, including:

Improved object manipulation: The neural network trained using this method can effectively manipulate 3D objects based on their perceived properties.
Realistic training data: Synthetic stereo images provide a realistic training environment, allowing the neural network to learn object manipulation in a more accurate and reliable manner.
Efficient depth estimation: The low-resolution disparity image generation helps in estimating the depth information of objects efficiently, enabling better object manipulation capabilities.

Original Abstract Submitted

A method for training a neural network to perform 3D object manipulation is described. The method includes extracting features from each image of a synthetic stereo pair of images. The method also includes generating a low-resolution disparity image based on the features extracted from each image of the synthetic stereo pair of images. The method further includes generating, by the neural network, a feature map based on the low-resolution disparity image and one of the synthetic stereo pair of images. The method also includes manipulating an unknown object perceived from the feature map according to a perception prediction from a prediction head.

17839193. SYSTEM AND METHOD FOR UNKNOWN OBJECT MANIPULATION FROM PURE SYNTHETIC STEREO DATA simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)

Contents

SYSTEM AND METHOD FOR UNKNOWN OBJECT MANIPULATION FROM PURE SYNTHETIC STEREO DATA

Organization Name

Inventor(s)