View source for 17945325. SEMANTIC SEGMENTATION NEURAL NETWORK FOR POINT CLOUDS simplified abstract (WAYMO LLC)

=SEMANTIC SEGMENTATION NEURAL NETWORK FOR POINT CLOUDS=


==Organization Name==

[[:Category:WAYMO LLC|WAYMO LLC]]


[[Category:WAYMO LLC]]

==Inventor(s)==

[[:Category:Yin Zhou of San Jose CA (US)|Yin Zhou of San Jose CA (US)]][[Category:Yin Zhou of San Jose CA (US)]]

[[:Category:Ruizhongtai Qi of Mountain View CA (US)|Ruizhongtai Qi of Mountain View CA (US)]][[Category:Ruizhongtai Qi of Mountain View CA (US)]]

[[:Category:Dragomir Anguelov of San Francisco CA (US)|Dragomir Anguelov of San Francisco CA (US)]][[Category:Dragomir Anguelov of San Francisco CA (US)]]

[[:Category:Minghua Liu of La Jolla CA (US)|Minghua Liu of La Jolla CA (US)]][[Category:Minghua Liu of La Jolla CA (US)]]

[[:Category:Boqing Gong of Bellevue WA (US)|Boqing Gong of Bellevue WA (US)]][[Category:Boqing Gong of Bellevue WA (US)]]

==SEMANTIC SEGMENTATION NEURAL NETWORK FOR POINT CLOUDS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17945325 titled 'SEMANTIC SEGMENTATION NEURAL NETWORK FOR POINT CLOUDS

==Simplified Explanation==

The patent application describes methods, systems, and apparatus for training a semantic segmentation neural network for point clouds. Here is a simplified explanation of the abstract:

* Obtaining a plurality of training points divided into components.
* Obtaining ground truth category data for each component.
* Processing training points using a neural network to generate semantic segmentation.
* Determining a gradient of a loss function penalizing non-ground truth categories.
* Updating neural network parameters using the gradient.

== Potential Applications ==
This technology could be applied in various fields such as autonomous driving, robotics, and augmented reality for accurate object detection and scene understanding.

== Problems Solved ==
This technology solves the problem of efficiently training neural networks to perform semantic segmentation on point cloud data, which is crucial for tasks like object recognition and scene understanding.

== Benefits ==
The benefits of this technology include improved accuracy in semantic segmentation, faster training times, and better performance in real-world applications.

== Potential Commercial Applications ==
One potential commercial application of this technology could be in the development of advanced driver-assistance systems (ADAS) for autonomous vehicles, where accurate object detection and scene understanding are essential.

== Possible Prior Art ==
Prior art in this field may include research papers or patents related to semantic segmentation of point cloud data using neural networks. One example could be a study on improving the efficiency of training neural networks for semantic segmentation tasks.

=== Unanswered Questions ===

=== How does this technology compare to existing methods for training semantic segmentation neural networks on point clouds? ===

This article does not provide a direct comparison with existing methods or technologies in the field. It would be beneficial to understand the specific advantages and limitations of this approach compared to traditional methods.

=== What are the potential limitations or challenges of implementing this technology in real-world applications? ===

The article does not address the potential obstacles or difficulties that may arise when implementing this technology in practical scenarios. It would be important to consider factors such as computational resources, data complexity, and scalability issues.


==Original Abstract Submitted==

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a semantic segmentation neural network for point clouds. One of the methods includes: obtaining a plurality of training points divided into a respective plurality of components; obtaining, for each of the respective plurality of components, data identifying a ground truth category for one or more labeled point; processing each training points using a semantic segmentation neural network to generate a semantic segmentation that includes a respective score for each of the plurality of categories; determining a gradient of a loss function that penalizes the semantic segmentation neural network for generating, for points in the component, non-zero scores for categories that are not the ground truth category for any labeled point in the component; and updating, using the gradient, the parameters of the semantic segmentation neural network.



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