18012264. Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes simplified abstract (GOOGLE LLC)

Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes

Organization Name

GOOGLE LLC

Inventor(s)

Daniel Christopher Duckworth of Berlin (DE)

Suhani Deepak-Ranu Vora of San Mateo CA (US)

Noha Radwan of Zurich (CH)

Klaus Greff of Berlin (DE)

Henning Meyer of Berlin (DE)

Kyle Adam Genova of San Mateo CA (US)

Seyed Mohammad Mehdi Sajjadi of Berlin (DE)

Etienne François Régis Pot of Berlin (DE)

Andrea Tagliasacchi of Victoria (CA)

Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes - A simplified explanation of the abstract

This abstract first appeared for US patent application 18012264 titled 'Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes

Simplified Explanation

The present disclosure describes a method for constructing a three-dimensional semantic segmentation of a scene from two-dimensional inputs using a machine-learned semantic segmentation model framework.

• Obtaining an image set of one or more views of a subject scene.
• Generating a scene representation in three dimensions based on the image set.
• Generating a multidimensional field of probability distributions over semantic categories defined over the three dimensions of the subject scene using a machine-learned model.
• Outputting classification data for at least one location in the subject scene.

Potential Applications

This technology could be applied in various fields such as autonomous driving, robotics, augmented reality, and virtual reality for scene understanding and object recognition.

Problems Solved

This technology solves the problem of accurately segmenting and understanding a scene in three dimensions from two-dimensional inputs, improving the performance of computer vision systems.

Benefits

The benefits of this technology include improved accuracy in scene understanding, enhanced object recognition, and better decision-making capabilities for autonomous systems.

Potential Commercial Applications

Potential commercial applications of this technology include autonomous vehicles, surveillance systems, industrial automation, and virtual reality applications.

Possible Prior Art

One possible prior art in this field is the work on semantic segmentation using deep learning models in computer vision applications.

What are the limitations of the proposed method in terms of scalability and real-time processing?

The proposed method may face challenges in processing large-scale scenes with a high number of semantic categories in real-time due to the computational complexity involved.

How does the accuracy of the semantic segmentation compare to existing methods in similar applications?

The accuracy of the semantic segmentation achieved by this method may vary depending on the complexity of the scene and the quality of the input images. Further comparative studies with existing methods are needed to evaluate its performance.

Original Abstract Submitted

Example embodiments of the present disclosure provide an example computer-implemented method for constructing a three-dimensional semantic segmentation of a scene from two-dimensional inputs. The example method includes obtaining, by a computing system comprising one or more processors, an image set comprising one or more views of a subject scene. The example method includes generating, by the computing system and based at least in part on the image set, a scene representation describing the subject scene in three dimensions. The example method includes generating, by the computing system and using a machine-learned semantic segmentation model framework, a multidimensional field of probability distributions over semantic categories, the multidimensional field defined over the three dimensions of the subject scene. The example method includes outputting, by the computing system, classification data for at least one location in the subject scene.